HDLSS Asy’s: Geometrical Represent’n Assume, let Study Subspace Generated by Data Hyperplane...

144
HDLSS Asy’s: Geometrical Represent’n Assume , let Study Subspace Generated by Data Hyperplane through 0, of dimension Points are “nearly equidistant to 0”, & dist Within plane, can “rotate towards Unit Simplex” All Gaussian data sets are: “near Unit Simplex Vertices”!!! Hall, Marron & Neeman (2005) n d d d n I N Z Z , 0 ~ ,..., 1 d d

Transcript of HDLSS Asy’s: Geometrical Represent’n Assume, let Study Subspace Generated by Data Hyperplane...

HDLSS Asyrsquos Geometrical Representrsquon

Assume let

Study Subspace Generated by Data

Hyperplane through 0

of dimension

Points are ldquonearly equidistant to 0rdquo

amp dist

Within plane can

ldquorotate towards Unit Simplexrdquo

All Gaussian data sets are

ldquonear Unit Simplex Verticesrdquo

ldquoRandomnessrdquo appears

only in rotation of simplex

n

d ddn INZZ 0~1

d

d

Hall Marron amp Neeman (2005)

HDLSS Asyrsquos Geometrical Represenrsquotion

Explanation of Observed (Simulation) Behavior

ldquoeverything similar for very high d rdquo

bull 2 popnrsquos are 2 simplices (ie regular n-hedrons)bull All are same distance from the other classbull ie everything is a support vectorbull ie all sensible directions show ldquodata pilingrdquobull so ldquosensible methods are all nearly the samerdquo

2nd Paper on HDLSS Asymptotics

Notes on Kentrsquos Normal Scale Mixture

bull Data Vectors are indeprsquodent of each other

bull But entries of each have strong dependrsquoce

bull However can show entries have cov = 0

bull Recall statistical folklore

Covariance = 0 Independence

ddddiININX 10050050~

0 Covariance is not independence

Simple Example c to make cov(XY) = 0

0 Covariance is not independence

Result

bull Joint distribution of and ndash Has Gaussian marginals

ndash Has

ndash Yet strong dependence of and

ndash Thus not multivariate Gaussian

Shows Multivariate Gaussian means more

than Gaussian Marginals

YX

0cov YX

X Y

HDLSS Asyrsquos Geometrical RepresenrsquotionFurther Consequences of Geometric Represenrsquotion

1 DWD more stable than SVM(based on deeper limiting distributions)

(reflects intuitive idea feeling sampling variation)(something like mean vs median)

Hall Marron Neeman (2005)

2 1-NN rule inefficiency is quantified Hall Marron Neeman (2005)

3 Inefficiency of DWD for uneven sample size(motivates weighted version)

Qiao et al (2010)

HDLSS Math Stat of PCA

Consistency amp Strong Inconsistency

Spike Covariance Model Paul (2007)

For Eigenvalues

1st Eigenvector

How Good are Empirical Versions

as Estimates

11 21 dddd d

1u

11 ˆˆˆ uddd

Consistency (big enough spike)

For

Strong Inconsistency (spike not big enough)

For

1

0ˆ 11 uuAngle

1

011 90ˆ uuAngle

HDLSS Math Stat of PCA

PC Scores (ie projections)

Not Consistent

So how can PCA find Useful Signals in Data

Key is ldquoProportional Errorsrdquo

Axes have Inconsistent Scales

But Relationships are Still Useful

HDLSS Math Stat of PCA

jji

ji Rs

s

Interesting Question

Behavior in Very High Dimension

Answer El Karoui (2010)

bull In Random Matrix Limit

bull Kernel Embedded Classifiers ~

~ Linear Classifiers

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

So not Clear Embedding Helps

Thus not yet Implemented in DWD

HDLSS Asymptotics amp Kernel Methods

HDLSS Additional Results

Batch Adjustment Xuxin Liu

Recall Intuition from above

Key is sizes of biological subtypes

Differing ratio trips up mean

But DWD more robust

Mathematics behind this

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Asyrsquos Geometrical Represenrsquotion

Explanation of Observed (Simulation) Behavior

ldquoeverything similar for very high d rdquo

bull 2 popnrsquos are 2 simplices (ie regular n-hedrons)bull All are same distance from the other classbull ie everything is a support vectorbull ie all sensible directions show ldquodata pilingrdquobull so ldquosensible methods are all nearly the samerdquo

2nd Paper on HDLSS Asymptotics

Notes on Kentrsquos Normal Scale Mixture

bull Data Vectors are indeprsquodent of each other

bull But entries of each have strong dependrsquoce

bull However can show entries have cov = 0

bull Recall statistical folklore

Covariance = 0 Independence

ddddiININX 10050050~

0 Covariance is not independence

Simple Example c to make cov(XY) = 0

0 Covariance is not independence

Result

bull Joint distribution of and ndash Has Gaussian marginals

ndash Has

ndash Yet strong dependence of and

ndash Thus not multivariate Gaussian

Shows Multivariate Gaussian means more

than Gaussian Marginals

YX

0cov YX

X Y

HDLSS Asyrsquos Geometrical RepresenrsquotionFurther Consequences of Geometric Represenrsquotion

1 DWD more stable than SVM(based on deeper limiting distributions)

(reflects intuitive idea feeling sampling variation)(something like mean vs median)

Hall Marron Neeman (2005)

2 1-NN rule inefficiency is quantified Hall Marron Neeman (2005)

3 Inefficiency of DWD for uneven sample size(motivates weighted version)

Qiao et al (2010)

HDLSS Math Stat of PCA

Consistency amp Strong Inconsistency

Spike Covariance Model Paul (2007)

For Eigenvalues

1st Eigenvector

How Good are Empirical Versions

as Estimates

11 21 dddd d

1u

11 ˆˆˆ uddd

Consistency (big enough spike)

For

Strong Inconsistency (spike not big enough)

For

1

0ˆ 11 uuAngle

1

011 90ˆ uuAngle

HDLSS Math Stat of PCA

PC Scores (ie projections)

Not Consistent

So how can PCA find Useful Signals in Data

Key is ldquoProportional Errorsrdquo

Axes have Inconsistent Scales

But Relationships are Still Useful

HDLSS Math Stat of PCA

jji

ji Rs

s

Interesting Question

Behavior in Very High Dimension

Answer El Karoui (2010)

bull In Random Matrix Limit

bull Kernel Embedded Classifiers ~

~ Linear Classifiers

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

So not Clear Embedding Helps

Thus not yet Implemented in DWD

HDLSS Asymptotics amp Kernel Methods

HDLSS Additional Results

Batch Adjustment Xuxin Liu

Recall Intuition from above

Key is sizes of biological subtypes

Differing ratio trips up mean

But DWD more robust

Mathematics behind this

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

2nd Paper on HDLSS Asymptotics

Notes on Kentrsquos Normal Scale Mixture

bull Data Vectors are indeprsquodent of each other

bull But entries of each have strong dependrsquoce

bull However can show entries have cov = 0

bull Recall statistical folklore

Covariance = 0 Independence

ddddiININX 10050050~

0 Covariance is not independence

Simple Example c to make cov(XY) = 0

0 Covariance is not independence

Result

bull Joint distribution of and ndash Has Gaussian marginals

ndash Has

ndash Yet strong dependence of and

ndash Thus not multivariate Gaussian

Shows Multivariate Gaussian means more

than Gaussian Marginals

YX

0cov YX

X Y

HDLSS Asyrsquos Geometrical RepresenrsquotionFurther Consequences of Geometric Represenrsquotion

1 DWD more stable than SVM(based on deeper limiting distributions)

(reflects intuitive idea feeling sampling variation)(something like mean vs median)

Hall Marron Neeman (2005)

2 1-NN rule inefficiency is quantified Hall Marron Neeman (2005)

3 Inefficiency of DWD for uneven sample size(motivates weighted version)

Qiao et al (2010)

HDLSS Math Stat of PCA

Consistency amp Strong Inconsistency

Spike Covariance Model Paul (2007)

For Eigenvalues

1st Eigenvector

How Good are Empirical Versions

as Estimates

11 21 dddd d

1u

11 ˆˆˆ uddd

Consistency (big enough spike)

For

Strong Inconsistency (spike not big enough)

For

1

0ˆ 11 uuAngle

1

011 90ˆ uuAngle

HDLSS Math Stat of PCA

PC Scores (ie projections)

Not Consistent

So how can PCA find Useful Signals in Data

Key is ldquoProportional Errorsrdquo

Axes have Inconsistent Scales

But Relationships are Still Useful

HDLSS Math Stat of PCA

jji

ji Rs

s

Interesting Question

Behavior in Very High Dimension

Answer El Karoui (2010)

bull In Random Matrix Limit

bull Kernel Embedded Classifiers ~

~ Linear Classifiers

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

So not Clear Embedding Helps

Thus not yet Implemented in DWD

HDLSS Asymptotics amp Kernel Methods

HDLSS Additional Results

Batch Adjustment Xuxin Liu

Recall Intuition from above

Key is sizes of biological subtypes

Differing ratio trips up mean

But DWD more robust

Mathematics behind this

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

0 Covariance is not independence

Simple Example c to make cov(XY) = 0

0 Covariance is not independence

Result

bull Joint distribution of and ndash Has Gaussian marginals

ndash Has

ndash Yet strong dependence of and

ndash Thus not multivariate Gaussian

Shows Multivariate Gaussian means more

than Gaussian Marginals

YX

0cov YX

X Y

HDLSS Asyrsquos Geometrical RepresenrsquotionFurther Consequences of Geometric Represenrsquotion

1 DWD more stable than SVM(based on deeper limiting distributions)

(reflects intuitive idea feeling sampling variation)(something like mean vs median)

Hall Marron Neeman (2005)

2 1-NN rule inefficiency is quantified Hall Marron Neeman (2005)

3 Inefficiency of DWD for uneven sample size(motivates weighted version)

Qiao et al (2010)

HDLSS Math Stat of PCA

Consistency amp Strong Inconsistency

Spike Covariance Model Paul (2007)

For Eigenvalues

1st Eigenvector

How Good are Empirical Versions

as Estimates

11 21 dddd d

1u

11 ˆˆˆ uddd

Consistency (big enough spike)

For

Strong Inconsistency (spike not big enough)

For

1

0ˆ 11 uuAngle

1

011 90ˆ uuAngle

HDLSS Math Stat of PCA

PC Scores (ie projections)

Not Consistent

So how can PCA find Useful Signals in Data

Key is ldquoProportional Errorsrdquo

Axes have Inconsistent Scales

But Relationships are Still Useful

HDLSS Math Stat of PCA

jji

ji Rs

s

Interesting Question

Behavior in Very High Dimension

Answer El Karoui (2010)

bull In Random Matrix Limit

bull Kernel Embedded Classifiers ~

~ Linear Classifiers

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

So not Clear Embedding Helps

Thus not yet Implemented in DWD

HDLSS Asymptotics amp Kernel Methods

HDLSS Additional Results

Batch Adjustment Xuxin Liu

Recall Intuition from above

Key is sizes of biological subtypes

Differing ratio trips up mean

But DWD more robust

Mathematics behind this

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

0 Covariance is not independence

Result

bull Joint distribution of and ndash Has Gaussian marginals

ndash Has

ndash Yet strong dependence of and

ndash Thus not multivariate Gaussian

Shows Multivariate Gaussian means more

than Gaussian Marginals

YX

0cov YX

X Y

HDLSS Asyrsquos Geometrical RepresenrsquotionFurther Consequences of Geometric Represenrsquotion

1 DWD more stable than SVM(based on deeper limiting distributions)

(reflects intuitive idea feeling sampling variation)(something like mean vs median)

Hall Marron Neeman (2005)

2 1-NN rule inefficiency is quantified Hall Marron Neeman (2005)

3 Inefficiency of DWD for uneven sample size(motivates weighted version)

Qiao et al (2010)

HDLSS Math Stat of PCA

Consistency amp Strong Inconsistency

Spike Covariance Model Paul (2007)

For Eigenvalues

1st Eigenvector

How Good are Empirical Versions

as Estimates

11 21 dddd d

1u

11 ˆˆˆ uddd

Consistency (big enough spike)

For

Strong Inconsistency (spike not big enough)

For

1

0ˆ 11 uuAngle

1

011 90ˆ uuAngle

HDLSS Math Stat of PCA

PC Scores (ie projections)

Not Consistent

So how can PCA find Useful Signals in Data

Key is ldquoProportional Errorsrdquo

Axes have Inconsistent Scales

But Relationships are Still Useful

HDLSS Math Stat of PCA

jji

ji Rs

s

Interesting Question

Behavior in Very High Dimension

Answer El Karoui (2010)

bull In Random Matrix Limit

bull Kernel Embedded Classifiers ~

~ Linear Classifiers

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

So not Clear Embedding Helps

Thus not yet Implemented in DWD

HDLSS Asymptotics amp Kernel Methods

HDLSS Additional Results

Batch Adjustment Xuxin Liu

Recall Intuition from above

Key is sizes of biological subtypes

Differing ratio trips up mean

But DWD more robust

Mathematics behind this

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Asyrsquos Geometrical RepresenrsquotionFurther Consequences of Geometric Represenrsquotion

1 DWD more stable than SVM(based on deeper limiting distributions)

(reflects intuitive idea feeling sampling variation)(something like mean vs median)

Hall Marron Neeman (2005)

2 1-NN rule inefficiency is quantified Hall Marron Neeman (2005)

3 Inefficiency of DWD for uneven sample size(motivates weighted version)

Qiao et al (2010)

HDLSS Math Stat of PCA

Consistency amp Strong Inconsistency

Spike Covariance Model Paul (2007)

For Eigenvalues

1st Eigenvector

How Good are Empirical Versions

as Estimates

11 21 dddd d

1u

11 ˆˆˆ uddd

Consistency (big enough spike)

For

Strong Inconsistency (spike not big enough)

For

1

0ˆ 11 uuAngle

1

011 90ˆ uuAngle

HDLSS Math Stat of PCA

PC Scores (ie projections)

Not Consistent

So how can PCA find Useful Signals in Data

Key is ldquoProportional Errorsrdquo

Axes have Inconsistent Scales

But Relationships are Still Useful

HDLSS Math Stat of PCA

jji

ji Rs

s

Interesting Question

Behavior in Very High Dimension

Answer El Karoui (2010)

bull In Random Matrix Limit

bull Kernel Embedded Classifiers ~

~ Linear Classifiers

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

So not Clear Embedding Helps

Thus not yet Implemented in DWD

HDLSS Asymptotics amp Kernel Methods

HDLSS Additional Results

Batch Adjustment Xuxin Liu

Recall Intuition from above

Key is sizes of biological subtypes

Differing ratio trips up mean

But DWD more robust

Mathematics behind this

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Math Stat of PCA

Consistency amp Strong Inconsistency

Spike Covariance Model Paul (2007)

For Eigenvalues

1st Eigenvector

How Good are Empirical Versions

as Estimates

11 21 dddd d

1u

11 ˆˆˆ uddd

Consistency (big enough spike)

For

Strong Inconsistency (spike not big enough)

For

1

0ˆ 11 uuAngle

1

011 90ˆ uuAngle

HDLSS Math Stat of PCA

PC Scores (ie projections)

Not Consistent

So how can PCA find Useful Signals in Data

Key is ldquoProportional Errorsrdquo

Axes have Inconsistent Scales

But Relationships are Still Useful

HDLSS Math Stat of PCA

jji

ji Rs

s

Interesting Question

Behavior in Very High Dimension

Answer El Karoui (2010)

bull In Random Matrix Limit

bull Kernel Embedded Classifiers ~

~ Linear Classifiers

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

So not Clear Embedding Helps

Thus not yet Implemented in DWD

HDLSS Asymptotics amp Kernel Methods

HDLSS Additional Results

Batch Adjustment Xuxin Liu

Recall Intuition from above

Key is sizes of biological subtypes

Differing ratio trips up mean

But DWD more robust

Mathematics behind this

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

Consistency (big enough spike)

For

Strong Inconsistency (spike not big enough)

For

1

0ˆ 11 uuAngle

1

011 90ˆ uuAngle

HDLSS Math Stat of PCA

PC Scores (ie projections)

Not Consistent

So how can PCA find Useful Signals in Data

Key is ldquoProportional Errorsrdquo

Axes have Inconsistent Scales

But Relationships are Still Useful

HDLSS Math Stat of PCA

jji

ji Rs

s

Interesting Question

Behavior in Very High Dimension

Answer El Karoui (2010)

bull In Random Matrix Limit

bull Kernel Embedded Classifiers ~

~ Linear Classifiers

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

So not Clear Embedding Helps

Thus not yet Implemented in DWD

HDLSS Asymptotics amp Kernel Methods

HDLSS Additional Results

Batch Adjustment Xuxin Liu

Recall Intuition from above

Key is sizes of biological subtypes

Differing ratio trips up mean

But DWD more robust

Mathematics behind this

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

PC Scores (ie projections)

Not Consistent

So how can PCA find Useful Signals in Data

Key is ldquoProportional Errorsrdquo

Axes have Inconsistent Scales

But Relationships are Still Useful

HDLSS Math Stat of PCA

jji

ji Rs

s

Interesting Question

Behavior in Very High Dimension

Answer El Karoui (2010)

bull In Random Matrix Limit

bull Kernel Embedded Classifiers ~

~ Linear Classifiers

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

So not Clear Embedding Helps

Thus not yet Implemented in DWD

HDLSS Asymptotics amp Kernel Methods

HDLSS Additional Results

Batch Adjustment Xuxin Liu

Recall Intuition from above

Key is sizes of biological subtypes

Differing ratio trips up mean

But DWD more robust

Mathematics behind this

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

Interesting Question

Behavior in Very High Dimension

Answer El Karoui (2010)

bull In Random Matrix Limit

bull Kernel Embedded Classifiers ~

~ Linear Classifiers

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

So not Clear Embedding Helps

Thus not yet Implemented in DWD

HDLSS Asymptotics amp Kernel Methods

HDLSS Additional Results

Batch Adjustment Xuxin Liu

Recall Intuition from above

Key is sizes of biological subtypes

Differing ratio trips up mean

But DWD more robust

Mathematics behind this

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

HDLSS Asymptotics amp Kernel Methods

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

So not Clear Embedding Helps

Thus not yet Implemented in DWD

HDLSS Asymptotics amp Kernel Methods

HDLSS Additional Results

Batch Adjustment Xuxin Liu

Recall Intuition from above

Key is sizes of biological subtypes

Differing ratio trips up mean

But DWD more robust

Mathematics behind this

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

Interesting Question

Behavior in Very High Dimension

Implications for DWD

Recall Main Advantage is for High d

So not Clear Embedding Helps

Thus not yet Implemented in DWD

HDLSS Asymptotics amp Kernel Methods

HDLSS Additional Results

Batch Adjustment Xuxin Liu

Recall Intuition from above

Key is sizes of biological subtypes

Differing ratio trips up mean

But DWD more robust

Mathematics behind this

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Additional Results

Batch Adjustment Xuxin Liu

Recall Intuition from above

Key is sizes of biological subtypes

Differing ratio trips up mean

But DWD more robust

Mathematics behind this

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

Liu Twiddle ratios of subtypes

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Data Combo Mathematics

Xuxin Liu Dissertation Results

Simple Unbalanced Cluster Model

Growing at rate as

Answers depend on

Visualization of settinghellip

d d

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Data Combo Mathematics

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Data Combo Mathematics

Asymptotic Results (as )

Let denote ratio between subgroup sizes

d

r

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For PAM Inconsistent

Angle(PAMTruth)

For PAM Strongly Inconsistent

Angle(PAMTruth)

d

2

1

2

1

0 rC

090

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Data Combo Mathematics

Asymptotic Results (as )

For DWD Inconsistent

Angle(DWDTruth)

For DWD Strongly Inconsistent

Angle(DWDTruth)

d

2

1

2

1

090

0 rC

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Data Combo Mathematics

Value of and for sample size ratio

only when

Otherwise for both are Inconsistent

rC

22

1cos

2

1

r

rCr

0 rr CC

r

1r

1r

rC

22

1cos

32

31

r

rCr

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and rCrC

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

HDLSS Data Combo Mathematics

Comparison between PAM and DWD

Ie between and

Shows Strong Difference

Explains Above Empirical Observation

rCrC

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

Personal Observations

HDLSS world ishellip

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

HDLSS Asymptotics

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

Personal Observations

HDLSS world ishellip

Surprising (many times)

[Think Irsquove got it and then hellip]

Mathematically Beautiful ()

Practically Relevant

HDLSS Asymptotics

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

The Future of HDLSS Asymptotics

bull ldquoContiguityrdquo in Hypo Testing

bull Rates of Convergence

bull Improvements of DWD

(eg other functions of distance than inverse)

bull Many Others

It is still early days hellip

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

State of HDLSS Research

DevelopmentOf Methods

MathematicalAssessment

hellip

(thanks todefiantcorbanedugtiptonnet-funiceberghtml)

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

Independent Component Analysis

Personal Viewpoint

Directions

(eg PCA DWD)

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

Independent Component Analysis

Personal Viewpoint

Directions that maximize independence

Motivating Context Signal Processing

ldquoBlind Source Separationrdquo

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

Independent Component Analysis

References

bull Cardoso (1993) (1st paper)

bull Lee (1998) (early book not reccorsquoed)

bull Hyvaumlrinen amp Oja (1998)

(excellent short tutorial)

bull Hyvaumlrinen Karhunen amp Oja (2001)

(detailed monograph)

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

ldquoCocktail party problemrdquo

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

ldquoCocktail party problemrdquo

bull Hear several simultaneous conversations

bull Would like to

separate them

Model for ldquoconversationsrdquo time series

and ts1 ts2

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Model for ldquoconversationsrdquo time series

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Mixed version of signals

And also a 2nd mixture

tsatsatx 2121111

tsatsatx 2221212

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Mixed version of signals

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Goal Recover signal

From data

For unknown mixture matrix

where for all

tx

txtx

2

1

ts

tsts

2

1

2221

1211

aa

aaA

sAx t

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Goal is to find separating weights

so that for allxWs

W

t

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Goal is to find separating weights

so that for all

Problem would be fine

but is unknown

xWs

W

1AW

t

A

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

= matrix of eigenvectorsW

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

1 PCA (on population of 2-d vectors)

Maximal

Variance

Minimal

Variance

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

2 ICA (will describe method later)

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Solutions for Cocktail Party Problem

1PCA (on population of 2-d vectors)

[direction of maximal variation

doesnrsquot solve this problem]

2ICA (will describe method later)

[Independent Components do solve it]

[modulo sign changes and identification]

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Recall original time series

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating ExampleRelation to OODA recall data matrix

dnd

n

n

XX

XX

XXX

1

111

1

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating ExampleRelation to OODA recall data matrix

Signal Processing focus on rows

( time series indexed by )

OODA focus on columns as data objects

( data vectors)

Note 2 viewpoints like ldquodualsrdquo for PCA

dnd

n

n

XX

XX

XXX

1

111

1

d nt 1

n

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals nttsts 1)()( 21

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Study Signals nttsts 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

nttsts 1)()( 21

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Signals - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Study Data nttxtx 1)()( 21

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Scatterplot View (signal processing) Study

bull Signals

bull Corresponding Scatterplot

bull Data

bull Corresponding Scatterplot

nttsts 1)()( 21

nttxtx 1)()( 21

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Data - Corresponding Scatterplot

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Scatterplot View (signal processing) Plot

bull Signals amp Scatrsquoplot

bull Data amp Scatrsquoplot

bull Scatterplots give hint how ICA is possible

bull Affine transformation

Stretches indeprsquot signals into deprsquot

bull Inversion is key to ICA

(even w unknown)

nttsts 1)()( 21

nttxtx 1)()( 21

A

sAx

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Signals - Corresponding Scatterplot

Note Independent

Since Known Value

Of s1 Does Not

Change Distribution

of s2

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Data - Corresponding Scatterplot

Note Dependent

Since Known Value

Of s1 Changes

Distribution of s2

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

PCA - Finds direction of greatest variation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

PCA - Wrong for signal separation

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Motivating Example

Why not PCA

Finds direction of greatest variation

Which is wrong for signal separation

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmICA Step 1bull sphere the data

(shown on right in scatterplot view)

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmICA Step 1 sphere the data

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work with

0 I ˆ 21 XZ

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmICA Step 1bull ldquosphere the datardquo

(shown on right in scatterplot view)bull ie find linear transfrsquon to make

mean = cov = bull ie work withbull requires of full rank

(at least ie no HDLSS)bull search for independent beyond

linear and quadratic structure

0 I ˆ 21 XZ

dnX

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possible

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmICA Step 2 Cocktail party example

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmICA Step 2bull Find dirrsquons that make (sphered) data

independent as possiblebull Recall ldquoindependencerdquo means

joint distribution is product of marginalsbull In cocktail party example

ndash Happens only when support parallel to axesndash Otherwise have blank areas

but marginals are non-zero

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianity

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmParallel Idea (and key to algorithm) bull Find directions that maximize

non-Gaussianitybull Based on assumption starting from

independent coordinatesbull Note ldquomostrdquo projections are Gaussian

(since projection is ldquolinear combordquo)bull Mathematics behind this

Diaconis and Freedman (1984)

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Algorithm

Worst case for ICA Gaussian

bull Then sphered data are independent

bull So have independence in all (ortho) dirrsquons

bull Thus canrsquot find useful directions

bull Gaussian distribution is characterized by

Independent amp spherically symmetric

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmCriteria for non-Gaussianity

independence

bull Kurtosis

(4th order cumulant)bull Negative Entropybull Mutual Informationbull Nonparametric Maximum Likelihoodbull ldquoInfomaxrdquo in Neural Networksbull Interesting connections between these

224 3 EXEX

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iteratively

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmMatlab Algorithm (optimizing any of above)

ldquoFastICArdquobull Numerical gradient search methodbull Can find directions iterativelybull Or by simultaneous optimization

(note PCA does both but not ICA)bull Appears fast with good defaultsbull Careful about local optima

(Recco several random restarts)

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmFastICA Notational Summary

1First sphere data

2Apply ICA Find to make

rows of ldquoindeprsquotrdquo

3Can transform back to

original data scale

ˆ 21 XZ

SW

ZWS SS

SSS 21ˆˆ

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmCareful look at identifiability

bull Seen already in above example

bull Could rotate ldquosquare of datardquo in several ways etc

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmCareful look at identifiability

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmIdentifiability Swap Flips

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

(seen as swap above)

Since for a ldquopermutation matrixrdquo

(pre-multiplication by ldquoswaps rowsrdquo)

(post-multiplication by ldquoswaps columnsrdquo)

For each column ie

ie

SS S

P

PP

SS sAz

zPWzPAsP SSS 1

zAs SS

1

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmIdentifiability problem 1

Generally canrsquot order rows of (amp )

Since for a ldquopermutation matrixrdquo

For each column

So and are also ICA solutrsquons

(ie )

FastICA appears to order in terms of ldquohow non-Gaussianrdquo

SS S

PzPWzPAsP SSS

1

SPS SPW

ZPWPS SS

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmIdentifiability problem 2

Canrsquot find scale of elements of

(seen as flips above)

Since for a (full rank) diagonal matrix

(pre-multiplrsquon by is scalar multrsquon of rows)

(post-multiplrsquon by is scalar multrsquon of colrsquos)

Again for each colrsquon

ie

So and are also ICA solutions

D

s

SDW

D

D

SDSzDWzDAsD SSs

1

zAs SS

1

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA AlgorithmSignal Processing Scale identification

(Hyvaumlrinen and Oja 1999)

Choose scale so each signal

has ldquounit average energyrdquo

(preserves energy along rows of data matrix)

Explains ldquosame scalesrdquo in

Cocktail Party Example

)(tsi1)( 2

ti ts

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA amp Non-Gaussianity

Explore main ICA principle

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA amp Non-Gaussianity

Explore main ICA principle

Projections farther from coordinate axes

are more Gaussian

For the dirrsquon vector

where

(thus )

have for large and

kd

k

k

u

u

u

1

dkj

kjku ki

10

121

)10(NXud

t

k k d

1ku

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA amp Non-Gaussianity

Illustrative examples (d = 100 n = 500)

aUniform Marginals

bExponential Marginals

cBimodal Marginals

Study over range of values of k

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA amp Non-Gaussianity

Illustrative example - Uniform marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

aUniform Marginalsbull k = 1 very poor fit

(Uniform far from Gaussian)bull k = 2 much closer

(Triangular closer to Gaussian)bull k = 4 very close

but still have statrsquoly sigrsquot differencebull k gt= 6 all diffrsquos could be sampling varrsquon

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA amp Non-Gaussianity

Illustrative example - Exponential Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

b Exponential Marginalsbull still have convergence to Gaussian

but slower

(ldquoskewnessrdquo has stronger

impact than ldquokurtosisrdquo)bull now need k gt= 25 to see no difference

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA amp Non-Gaussianity

Illustrative example - Bimodal Marginals

>

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA amp Non-GaussianityIllustrative examples (d = 100 n = 500)

c Bimodal Marginalsbull Convergence to Gaussian

Surprisingly fast

bull Quite close for k = 9

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA amp Non-GaussianitySummary

For indep non-Gaussian

standardized rvrsquos

Projections farther from coordinate axes

are more Gaussian

dX

X

X 1

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA amp Non-GaussianityProjections farther from coordinate axes

are more Gaussian

Conclusions

I Expect most projrsquons are Gaussian

IINon-Grsquon projrsquons (ICA target) are special

IIIIs a given sample really ldquorandomrdquo

(could test)

IVHigh dimrsquoal space is a strange place

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Two Sine Waves ndash Original Signals

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Two Sine Waves ndash Original Scatterplot

Far

From

Indeprsquot

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Two Sine Waves ndash Mixed Input Data

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Two Sine Waves ndash Scatterplot amp PCA

Clearly

Wrong

Recovery

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Two Sine Waves ndash Scatterplot for ICA

Looks

Very

Good

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Two Sine Waves ndash ICA Reconstruction

Excellent

Despite

Non-Indeprsquot

Scatteplot

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Sine and Gaussian

Try Another Pair of Signals

More Like ldquoSignal + Noiserdquo

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Sine and Gaussian ndash Original Signals

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Sine and Gaussian ndash Original Scatterplot

Well

Set

For

ICA

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Sine and Gaussian ndash Mixed Input Data

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Sine and Gaussian ndash Scatterplot amp PCA

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Sine and Gaussian ndash PCA Reconstruction

Got Sine

Wave

+

Noise

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Sine and Gaussian ndash Scatterplot ICA

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Sine and Gaussian ndash ICA Reconstruction

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Both Gaussian

Try Another Pair of Signals

Understand Assumption of

One Not Gaussian

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Both Gaussian ndash Original Signals

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Both Gaussian ndash Original Scatterplot

Caution

Indeprsquot

In All

Directions

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Both Gaussian ndash Mixed Input Data

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Both Gaussian ndash PCA Scatterplot

Exploits

Variation

To Give

Good

Diredtions

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Both Gaussian ndash PCA Reconstruction

Looks

Good

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Both Gaussian ndash ICA Scatterplot

No Clear

Good

Rotation

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Both Gaussian ndash ICA Reconstruction

Is It

Bad

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Both Gaussian ndash Original Signals

Check

Against

Original

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Now Try FDA examples ndash Recall Parabolas

Curves As

Data Objects

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Now Try FDA examples ndash Parabolas

PCA Gives

Interpretable

Decomposition

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

Now Try FDA examples ndash Parabolas

Sphering

Loses

Structure

ICA Finds

Outliers

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Same Curves

plus ldquoOutliersrdquo

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

Impact all of

bull Mean

bull PC1 (slightly)

bull PC2 (dominant)

bull PC3 (tilt)

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

FDA example ndash Parabolas w 2 Outliers

ICA

Misses Main

Directions

But Finds

Outliers

(non-Gaussian)

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

FDA example ndash Parabolas Up and Down

Recall 2 Clear

Clusters

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

FDA example ndash Parabolas Up and Down

PCA

Clusters

amp Other

Structure

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA

Does Not

Find Clusters

Reason

Random Start

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Scary Issue

Local Minima in Optimization

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 1 Use PCA to Start

Worked Here

But Not

Always

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2

Use Multiple Random Starts

bull Shows When Have Multiple Minima

bull Range Should Turn Up Good Directions

bull More to Look At Interpret

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

3rd IC Dirrsquon

Looks Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

2nd

Looks

Good

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

More ICA Examples

FDA example ndash Parabolas Up and Down

ICA Solution 2 Multiple Random Starts

Never

Finds

Clusters

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview

ICA Overview

Interesting Method has Potential

Great for Directions of Non-Gaussianity

Eg Finding Outliers

Common Application Area FMRI

Has Its Costs

Slippery Optimization

Interpetation Challenges

  • HDLSS Asyrsquos Geometrical Representrsquon
  • HDLSS Asyrsquos Geometrical Represenrsquotion
  • 2nd Paper on HDLSS Asymptotics
  • 0 Covariance is not independence
  • 0 Covariance is not independence (2)
  • HDLSS Asyrsquos Geometrical Represenrsquotion (2)
  • HDLSS Math Stat of PCA
  • HDLSS Math Stat of PCA (2)
  • HDLSS Math Stat of PCA (3)
  • HDLSS Asymptotics amp Kernel Methods
  • HDLSS Asymptotics amp Kernel Methods (2)
  • HDLSS Asymptotics amp Kernel Methods (3)
  • HDLSS Additional Results
  • Liu Twiddle ratios of subtypes
  • HDLSS Data Combo Mathematics
  • HDLSS Data Combo Mathematics (2)
  • HDLSS Data Combo Mathematics (3)
  • HDLSS Data Combo Mathematics (4)
  • HDLSS Data Combo Mathematics (5)
  • HDLSS Data Combo Mathematics (6)
  • HDLSS Data Combo Mathematics (7)
  • HDLSS Data Combo Mathematics (8)
  • HDLSS Data Combo Mathematics (9)
  • HDLSS Data Combo Mathematics (10)
  • HDLSS Asymptotics
  • HDLSS Asymptotics (2)
  • HDLSS Asymptotics (3)
  • HDLSS Asymptotics (4)
  • The Future of HDLSS Asymptotics
  • State of HDLSS Research
  • Independent Component Analysis
  • Independent Component Analysis (2)
  • Independent Component Analysis (3)
  • Independent Component Analysis (4)
  • ICA Motivating Example
  • ICA Motivating Example (2)
  • ICA Motivating Example (3)
  • ICA Motivating Example (4)
  • ICA Motivating Example (5)
  • ICA Motivating Example (6)
  • ICA Motivating Example (7)
  • ICA Motivating Example (8)
  • ICA Motivating Example (9)
  • ICA Motivating Example (10)
  • ICA Motivating Example (11)
  • ICA Motivating Example (12)
  • ICA Motivating Example (13)
  • ICA Motivating Example (14)
  • ICA Motivating Example (15)
  • ICA Motivating Example (16)
  • ICA Motivating Example (17)
  • ICA Motivating Example (18)
  • ICA Motivating Example (19)
  • ICA Motivating Example (20)
  • ICA Motivating Example (21)
  • ICA Motivating Example (22)
  • ICA Motivating Example (23)
  • ICA Motivating Example (24)
  • ICA Motivating Example (25)
  • ICA Motivating Example (26)
  • ICA Motivating Example (27)
  • ICA Motivating Example (28)
  • ICA Motivating Example (29)
  • ICA Motivating Example (30)
  • ICA Motivating Example (31)
  • ICA Motivating Example (32)
  • ICA Motivating Example (33)
  • ICA Motivating Example (34)
  • ICA Algorithm
  • ICA Algorithm (2)
  • ICA Algorithm (3)
  • ICA Algorithm (4)
  • ICA Algorithm (5)
  • ICA Algorithm (6)
  • ICA Algorithm (7)
  • ICA Algorithm (8)
  • ICA Algorithm (9)
  • ICA Algorithm (10)
  • ICA Algorithm (11)
  • ICA Algorithm (12)
  • ICA Algorithm (13)
  • ICA Algorithm (14)
  • ICA Algorithm (15)
  • ICA Algorithm (16)
  • ICA Algorithm (17)
  • ICA Algorithm (18)
  • ICA Algorithm (19)
  • ICA Algorithm (20)
  • ICA Algorithm (21)
  • ICA Algorithm (22)
  • ICA Algorithm (23)
  • ICA Algorithm (24)
  • ICA Algorithm (25)
  • ICA amp Non-Gaussianity
  • ICA amp Non-Gaussianity (2)
  • ICA amp Non-Gaussianity (3)
  • ICA amp Non-Gaussianity (4)
  • ICA amp Non-Gaussianity (5)
  • ICA amp Non-Gaussianity (6)
  • ICA amp Non-Gaussianity (7)
  • ICA amp Non-Gaussianity (8)
  • ICA amp Non-Gaussianity (9)
  • ICA amp Non-Gaussianity (10)
  • ICA amp Non-Gaussianity (11)
  • More ICA Examples
  • More ICA Examples (2)
  • More ICA Examples (3)
  • More ICA Examples (4)
  • More ICA Examples (5)
  • More ICA Examples (6)
  • More ICA Examples (7)
  • More ICA Examples (8)
  • More ICA Examples (9)
  • More ICA Examples (10)
  • More ICA Examples (11)
  • More ICA Examples (12)
  • More ICA Examples (13)
  • More ICA Examples (14)
  • More ICA Examples (15)
  • More ICA Examples (16)
  • More ICA Examples (17)
  • More ICA Examples (18)
  • More ICA Examples (19)
  • More ICA Examples (20)
  • More ICA Examples (21)
  • More ICA Examples (22)
  • More ICA Examples (23)
  • More ICA Examples (24)
  • More ICA Examples (25)
  • More ICA Examples (26)
  • More ICA Examples (27)
  • More ICA Examples (28)
  • More ICA Examples (29)
  • More ICA Examples (30)
  • More ICA Examples (31)
  • More ICA Examples (32)
  • More ICA Examples (33)
  • More ICA Examples (34)
  • More ICA Examples (35)
  • More ICA Examples (36)
  • More ICA Examples (37)
  • More ICA Examples (38)
  • More ICA Examples (39)
  • ICA Overview