Feature Extraction with Data Miningzoe.bme.gatech.edu/~klee7/docs/HYU-IE-Sky.pdfFeature Extraction...

Feature Extraction with Data Mining:Introducing Sky’s research

Ph.D. Kichun “Sky” Lee

Post-doctoral Research Fellow, Emory University

October 31, 2011

Content

1 Introduction:an overview of research and presentation

2 Dependence Maps:a new dimensionality reduction

3 Semi-supervised Shrinkage Rule:a classification on wavelet domains

Introduction

Part I

Introduction:an overview of research and presentation

Kichun “Sky” Lee 10/31/2011 3/30 Feature Extraction with Data Mining

IntroductionResearch OverviewPresentation Overview

Education and Experiences

B.S. Industrial Management

Education

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011


(minor Electrical Engineering)

KAISTM.S. Industrial Engineering

(Human Computer Interaction)

KAIST

Ph.D. in Statistics

Industrial Systems Engineering

(minor Electrical Computer Engineering)

Georgia TechTeagu Science High School



Education and Experiences


Education

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011


(minor Electrical Engineering)

KAISTM.S. Industrial Engineering

(Human Computer Interaction)

KAIST

Ph.D. in Statistics

Industrial Systems Engineering

(minor Electrical Computer Engineering)

Georgia TechTeagu Science High School

IBM (internship)

ETRI (part-time engr. staff)

ICG (development head)

TmaxSoft (researcher)

Samsung SDS (researcher)

Georgia Tech, Emory

(post-doc)

Work Experience

Military Obligation



Research Methods

Information TechnologyInformation Technology

HCIHCI

System SWSystem SW



Research Methods

Data MiningData Mining

Semi-supervisedLearningSemi-supervisedLearning

Dim. ReductionDim. Reduction

SVMsSVMs

Multivariate StatisticsMultivariate Statistics


HCIHCI

System SWSystem SW



Research Methods

Data MiningData Mining Time SeriesTime Series


WaveletWavelet

Multi-scale methodsMulti-scale methods


SVMsSVMs

Multivariate StatisticsMultivariate Statistics FractalityFractality

Functional Data AnalysisFunctional Data Analysis



HCIHCI

System SWSystem SW



Research Methods

Data MiningData Mining


Time SeriesTime Series

WaveletWavelet

BioinformaticsMulti-scale methodsMulti-scale methods


SVMsSVMs

FractalityFractality

Functional Data AnalysisFunctional Data Analysis

Bioinformatics

Mgt. Science

Multivariate StatisticsMultivariate Statistics



HCIHCI

System SWSystem SW



Research Outputs

JournalsI Kichun Lee∗ and Brani Vidakovic. Semi-supervised Wavelet Shrinkage. Computational Statistics & Data Analysis, 2011

(Accepted).

I Youngja Park∗ , Dean P Jones, Thomas R Ziegler, Kichun Lee, Kavitha Kotha, Tianwei Yu, Greg S. Martin. Metabolic effectsof albumin therapy in acute lung injury measured by 1H-NMR spectroscopy of plasma: a pilot study. Critical Care Medicine,10:2308–2313, 2011.

I Pepa Ramirez Cobo, Kichun Sky Lee, Annalisa Molini, Amilcare Porporato, Gabriel Katul, Brani Vidakovic∗ . Wavelet-basedspectral methods for extracting self-similarity measures in time-varying two-dimensional rainfall maps. Journal of Time SeriesAnalysis, 32:337–446, 2011.

I Youngja Park, Ngoc-Anh Le, Tianwei Yu, Nana Gletsu Miller, Carolyn J. Accardi, Kichun S. Lee, Shaoxiong Wu, Thomas R.Ziegler, and Dean P. Jones∗ . Sulfur Amino Acid-Free Meal Increased Plasma Triglyceride as assessed by 1H-NMRSpectroscopy. Journal of Nutrition, 141:1424–1431, 2011.

I Sharla Gayle Patterson, Charlene W. Bayer, Robert J. Hendry, Nancy Sellers, K. Sky Lee, Brani Vidakovic, Boris Mizaikoff,Sheryl G.A. Gabram-Mendola∗ . Breath Analysis by Mass Spectrometry: A new Tool for Breast Cancer (BC) Detection? TheAmerican Surgeon, 77:747–751, 2011.

I Kichun Sky Lee∗ , Jongphil Kim, Brani Vidakovic. Regularity of Irregularity: Testing for Monofractality by Multifractal Tools.International Journal of Mathematics and Computer Science: Special Issue on Computational Biology and Data Mining, 2010(Accepted).

I Kichun Sky Lee∗ , M. Forrest Abouelnasr, Charlene W. Bayer, Sheryl G.A. Gabram, Boris Mizaikoff, Andre Rogatko, andBrani Vidakovic. Mining exhaled volatile organic compounds for breast cancer detection. Advances and Applications inStatistical Sciences, 1:327–342, 2009.

I Gordana Derado∗ , Kichun Lee, Orietta Nicolis, F. DuBois Bowman, Mary Newell, Fabrizio F. Rugger, and Brani Vidakovic.Wavelet-Based 3-D Multifractal Spectrum with Applications in Breast MRI Images, pages 281–292. Springer, 2008.

I H. Ahn∗ , K. Lee, and K. Kim. Global optimization of support vector machines using genetic algorithms for bankruptcyprediction. In Neural Information Processing, pages 420–429. Springer, 2006.



Research Outputs

Revision/SubmittedI Kichun Lee∗ and Alexander Gray. Dependency maps, a dimensionality reduction with dependency distance and

low-dimensional representation for high-dimensional data. Submitted to Data Mining and Knowledge Discovery (under minorrevision).

I Kichun Sky Lee, Jean Francois Coeurjolly, Brani Vidakovic. Variance estimation for fractional Brownian motions with fixedHurst parameters. Submitted to Communications in Statistics: Theory and methods

I Kichun Sky Lee∗ and Brani Vidakovic. Assessing time-changing hurst exponent and variance in mutifractional brownianmotion. Submitted to Annals of the Institute of Statistical Mathematics.

I Kichun Lee, John D. Carew, Jun-Hee Heu∗ . Recovering the boundary of a vessel wall from phase contrast magneticresonance images in low resolutions. Submitted to IEICE Transcationc on Information and Systems.

I Jongsu Lee, Chul-Yong Lee∗ , Kichun Lee. Forecasting Demand for a Newly Introduced Product Using Reservation PriceData and Bayesian Updating. Submitted to Industrial Marketing Management.

I Youngja H. Park, Kichun Lee, Quinlyn A. Soltow, Frederick H. Strobel, Kenneth L. Brigham, Richard E.Parker, Mark E.Wilson, Roy L. Sutliff, Keith G. Mansfield, Lynn M. Wachtman, Thomas R. Ziegler, Dean P. Jones∗ . Divergent behavior of 1environmental chemicals and endogenous metabolites in mammals. Submitted to Toxicology.

Working papersI Kichun Lee, Youngja Park, Dean P Jones∗ . Principal component loading statistics (PCLS) based feature selection for

discriminant analysis with PCA, PLS, and OPLS in spectroscopy data.I Youngja Park∗ , Kichun Lee, Thomas R. Ziegler, Gautam Habber, Brani Vidakovic, Dean P Jones. Assessment of nutritional

deficiency using Multifractal analysis on 1H NMR spectra of human plasma. In preparation for American Journal ofPhysiology-Regulatory Integrative and Comparative Physiology.

I Kyoung-jae Kim, Kichun Lee, Hyunchul Ahn∗ . Predicting financial distress for corporate risk management using SVM withtwo-dimensional reduction technique. In preparation for Information Science.



Research in IT

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Research in IT


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I Java EE 1.3 and 1.4 certificates, the first in the world (team award)



Research in IT


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IT Services: logistics, financial, health-care etc.

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Presentation Overview

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Fractality, Regularity

transform


Dimensionality Reduction

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Shrinkage

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SVMs Applications


Dependence Maps: a new dimensionality reduction

Part II

Dependence Maps:a new dimensionality reduction


Dependence Maps: a new dimensionality reduction

Part II

Dependence Maps:a new dimensionality reduction

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SVMs Applications


Dependence Maps: a new dimensionality reductionMotivationIntroduction of DependenceDependence Distance

Introduction of Dimensionality Reduction (DR)

I Goal?I Low dimensional representation of high dimensional data:

Ω = x1, . . . ,xn, xi ∈ Rm

find z1, . . . ,zn ∈ Rp such that pm



Introduction of Dimensionality Reduction (DR)

I Goal?I Low dimensional representation of high dimensional data:

Ω = x1, . . . ,xn, xi ∈ Rm

find z1, . . . ,zn ∈ Rp such that pm

m dimension p dimension

1x2x

1z2z

3x4x

3z4z

1nx 1nz1nnx

1nnz



Motivation for a new DR



Introduction of Dependence

Definition

Depdendency between xm ∈ X0 and xi ∈ Xt , denoted by Dept(m, i), isPr(Xt=xi ,X0=xm)

Pr(Xt=xi)Pr(X0=xm).

I Dept (m, i) < 1, negatively dependentI Dept (m, i) = 1, independentI Dept (m, i) > 1, positively dependent

I Assumption?I Each xi ∈ Ω is connected with others with its own “closenesses” to

otherså Consider connectivities of xi at t-th step Markovian transition

Q. How is it related to our goal?



Dependence Distance

I The dependence distance between xi and xj at t-step transition in Ωis defined:

D2t (i, j) =

n

∑m=1

(Dept(m, i)−Dept(m, j)

)2

transitions

x ixdependence

)( iXXD………… …mx ),( 0 iXmXDep t

xdependencejxp

),( 0 jXmXDep t

X X X X0X 1X 2X tX



Dependence Mapping

Theorem

D2t (i, j) =

n

∑m=1

λ2m

(vm(i)− vm(j)

)2,

vm are eigenvectors of P t(D(t))−1, P t is the t-th step transition probabilityand a diagonal matrix D(t) is defined by the column sum of P t :D(t)

kk = ∑n`=1 P t

`,k .

I By taking the largest p nontrivial eigenvalues of P t(D(t))−1

xi 7→(v1(i), . . . ,vp(i)

)Kichun “Sky” Lee 10/31/2011 15/30 Feature Extraction with Data Mining


Illustration of Dependence Mapping

Input LLE ISO Diffusion Dependence



Application to Volatile Organic Compounds (VOCs)

To diagnose cancerbased on VOCs of

their breathsubject index

VOCs

Case group

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Application to Volatile Organic Compounds (VOCs)

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Semi-supervised Shrinkage Rule: a classification on wavelet domains

Part III

Semi-supervised Shrinkage Rule:a classification on wavelet domains


Semi-supervised Shrinkage Rule: a classification on wavelet domains

Part III

Semi-supervised Shrinkage Rule:a classification on wavelet domains

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SVMs Applications


Semi-supervised Shrinkage Rule: a classification on wavelet domainsWavelet Data ShrinkageIntroduction of Semi-supervised LearningSemi-supervised Shrinkage

Wavelet Data Shrinkage: Hard-thresholding

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Idea of Semi-supervised Learning

I IdeaI Use both labeled and unlabeled observations

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SS Learning by Manifold Regularization

I Given empirical data

(x1,y1), . . . ,(x`,y`) ∈ X × 0,1,xl+1, . . . ,x`+u ∈ X ,

goal: Find a function f

f : X 7→ 0,1.

I Approach: Penalty V for labeled data + penalty along the topology ofX .

f = arg minf

‖f‖A=c

1`

`

∑i=1

V (xi ,yi , f ) + γI‖f‖2I .



SS Learning by Manifold Regularization

I Solution:I For V , an approximation: Fix f (xi ) = yi , i = 1, . . . , `I For ‖ ‖I , the graph Laplacian L = D−W

with adjacency matrix W = [Wij ] and D = diag∑`+uj=1 Wij

I Typically, Wij = exp−‖xi − xj‖2/σ if xi ,xj are in the neighborhood

f = arg minf

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1`

`

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`+u

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(f (xi)− f (xj)

)2Wij

= arg minf

‖f‖A=c

f˜T Lf˜ = [f`; −L−13 L2f`],

where f˜=(

f (x1), . . . , f (x`+u))T

and f` =(

f (x1), . . . , f (x`))T



Semi-supervised Shrinkage

I Select background shrinkage δ , e.g., hard thresholding withthresholds, λ1, and λ2; λ = σ

√(2 + τ) logN

I Estimator θ SS of SS for θ is given by

θSS(d |δ ,λ1,λ2) =

0 if |d |< λ1,δ (d)f (d˜) if λ1 ≤ |d | ≤ λ2,

δ (d) if |d | ≥ λ2

å Sort labeled coefficients (included, excluded) and unlabeled ones.

å Determine identities of unlabeled ones by “semi-supervised learning.”



Illustration of δ in SS Shrinkage

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Optimality of the SS Rule

I The oracle risk of diagonal projection (DP), which is unachievable,becomes

R(θ ,θ) = E ||θ −θ ||22 =N

∑i=1

min(θ2i ,σ

2) := Roracle(DP,θ)

Theorem

The SS estimator δ SS(d |δ hard ,λ1,λ2), as defined above, satisfies theinequality

R(δSS,θ)≤ L

σ

2 + Roracle(DP,θ)

for an L∼ logN and all θ ∈ RN , where λ1 and λ2 are sufficiently close toσ√

2 logN



Simulations: Noised Signals

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Simulations: After Shrinkage

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Comparisons with the Background Shrinkages

I Comparison (AMSE ratio) of SS based on its backgroundthresholding

I Ratio < 1 means SS performed better than its backgroundthresholding on average

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Relationship to Other Measurements

Table: Comparisons (AMSE ratio) of the SS rule with various backgroundshrinkages. Under-bared numbers mean SS performed better than itsbackground thresholding

ABE SS Rule BAMS SS Rule Hybrid SS Rule VisuShrink SS RuleBumps 1.934 2.063 1.951 1.948 2.600 2.532 2.939 2.702Blocks 1.008 .9196 .9093 .9071 2.031 1.818 1.148 1.009

HeaviSine .7119 .5189 .4071 .4072 .4004 .3836 .5131 .5497Doppler 1.018 .8907 .7964 .7955 1.238 1.185 1.076 1.053P.Reg. 1.341 1.296 1.118 1.117 1.621 1.565 1.654 1.615P.Poly. 1.756 1.832 1.555 1.555 2.105 2.058 2.641 2.517


I Conclusion

Data mining techniques + Feature extractionon domains of both original time and wavelet

ex:I Dependence-based dimensionality reductionI Semi-supervised shrinkage rules

Now what?

I Research plan

IT Service BT ServiceKnowledge

Service

I Research plan


Service

Data MiningData Mining Time SeriesTime SeriesData MiningData Mining Time SeriesTime Series


I Research plan



Service


Service Quality EngineeringService Quality Engineering


Knowledge engineeringKnowledge engineering

Business intelligenceBusiness intelligence

Data mining in IT/BT/Knowledge servicesData mining in IT/BT/Knowledge services

Systematic quality management, delivery, integration of IT/BT/Knowledge services

Systematic quality management, delivery, integration of IT/BT/Knowledge services

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Feature Extraction with Data Miningzoe.bme.gatech.edu/~klee7/docs/HYU-IE-Sky.pdfFeature Extraction...

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Transcript of Feature Extraction with Data Miningzoe.bme.gatech.edu/~klee7/docs/HYU-IE-Sky.pdfFeature Extraction...