Independent Subspace Analysisbapoczos/other_presentations/ISA_07_24_2007.pdf · 5 Independent...
Transcript of Independent Subspace Analysisbapoczos/other_presentations/ISA_07_24_2007.pdf · 5 Independent...
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Supervisor: Dr. András Lőrincz
Eötvös Loránd UniversityNeural Information Processing GroupBudapest, Hungary
Independent Subspace Analysis
Barnabás Póczos
MPI, Tübingen, 24 July 2007.
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Independent Component Analysis
Estimation
y(t)=Wx(t)
Sources Observation
x(t) = As(t)s(t)A
Mixing
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Some ICA Applications
Blind source separation Image denoising Medical signal processing – fMRI, ECG, EEG Modeling of the hippocampus Modeling of the visual cortex Feature extraction Face recognition Time series analysis Financial applications
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Independent Subspace Analysis
A 2 Rmd£md W 2Rmd£md
Sources Observation Estimation
s1 2Rd
s2 2Rd
sm 2 Rd
x1 2 Rd
x2 2 Rd
xm 2 Rd
y1 2 Rd
y2 2 Rd
ym 2 Rd
s sT ; : : : ; smT T 2 Rdm x xT ; : : : ; xmT T 2Rdm x As
y yT ; : : : ; ymT T 2Rdm yWx
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Independent Subspace Analysis The Efforts Cardoso, 1998
Conjecture, ICA preprocessing followed by permutation Akaho et al, 1999
Processing of EEG-fMRI data 2-dimensional Edgeworth-expansion
Bach & Jordan, 2003 kernel methods for mutual information estimation
Theis, 2003 Ambiguity issues, uniqueness of ISA
Hyvärinen & Köster, 2006 FastISA: A fast fixed-point algorithm for independent
subspace analysis
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Independent Subspace AnalysisThe Ambiguity
Ambiguity of ICA: Sources can be recovered only up to:
arbitrary permutation arbitrary scaling factors sign
Ambiguity of ISA: Sources can be recovered only up to:
arbitrary permutation arbitrary invertible transformation
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Independent Subspace Analysispairwise independence ≠ joint independence
In ICA case pairwise independence of the sources = joint independence of the sources (Comon, 1994)
In ISA case pairwise independence of the subspaces ≠ joint independence of the subspaces
Proof: Let 3 of 3-dimensional independent sources, where the elements of each subspace are pairwise independent.Than is a wrong ISA solution.
fs; s; sg; fs; s; sg; fs; s; sg
fs; s; sg; fs; s; sg; fs; s; sg
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The ISA Cost Functions
Mutual Information:
py pydyHy ¡R
Shannon-entropy:
RIy; : : : ;ym py
py¢¢¢pymdy
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The ISA Cost Functions
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Multidimensional Entropy Estimation
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Multi-dimensional Entropy Estimations, Method of Kozahenko and Leonenko
Then the nearest neighbour entropy estimation:
This estimation is means-square consistent, but not robust.Let us try to use more neighbours!
Hz n
nPj
nkN;j ¡ zjk CE
CE ¡1Re¡t tdt
fz; : : : ; zng n z 2 Rd N;j zj
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Multi-dimensional Entropy Estimations
Let us apply Rényi’s-entropy for estimating the Shannon-entropy:
H®¡®
R®!H® ¡
R
Let us use - K-nearest neighbors - geodesic spanning trees for estimating the multi-dimensional Rényi’s entropy.
f®zdz
fz fzdz
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Beadword - Halton - Hammersley Theorem
fz; : : : ; zng n z 2 Rd Nk;j k zj
° d¡ d®
¡®
kn®
nPj
Pv2Nk;j
kv¡zjk°! H®z c
n!1
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Multi-dimensional Entropy Estimations Using Geodesic Spanning Forests
Build first an Euclidean neighbourhood graph use the edges of the k nearest nodes to each node
Find geodesic spanning forests on this graph (minimal spanning forests of the Euclidean neighbourhood
graph)
zp
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Euclidean Graphs
Euclidean neighbourhood graph
Weight of minimal (γ-weighted) Euclidean spanning forest:
Where is the set of all γ-weighted Euclidean spanning forests
L°z T2T
Pe2T
kek°
T
fz; : : : ;zng n z 2 Rd
E fe ep; q zp¡ zq 2 Rd;zq 2 Nk;pg
° d¡ d® d°
L°zn® ! H®z c n!1
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Estimation of the Shannon-entropy
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Mutual Information Estimation
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Kernel covariance (KC) A. Gretton, R. Herbrich, A. Smola, F. Bach, M. Jordan
The calculation of the supremum over function sets is extremely difficult. We can ease it using Reproducing Kernel Hilbert Spaces.
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RKHS construction for x, y stochastic variables.
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Kernel covariance (KC)
And what is more, after some calculation we get, that
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The ISA Separation Theorem
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ISA Separation Theorem
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The ISA Separation Theorem
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Numerical Simulations
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Numerical Simulations 2D Letters (i.i.d.)
Sources ObservationEstimated sources
Performance matrix
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Numerical Simulations3D Curves (i.i.d.)
Sources ObservationEstimated sources
Performance matrix
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Numerical SimulationsFacial images (i.i.d.)
Sources ObservationEstimated sources
Performance matrix
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Numerical Simulations
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These methods for entropy estimation need i.i.d. processes.
What can we do with τ-order AR sources?
Then the innovations are i.i.d. processes:
and the mixing matrix is the same for the innovations, so we can use ISA on innovations.
sit Fsit ¡ : : : F¿ sit¡ ¿ ¹t
sit sit¡Esitjsit¡ ; sit¡ : : : ¹t
Ast xt ¡Extjxt¡ ; xt ¡ : : : xt
Working on Innovations
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Results Using Innovations
Performance using innovations
Estimated sourcesby plain ISA
Original ‘AR’ sources
Mixed sources
Estimated sourcesusing ISA on innovations
Performance of plain ISA
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Undercomplete Blind Subspace Deconvolution
Multi-dimensional generalization of the undercomplete Blind Source Deconvolution (BSD)
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BSSD reduction to ISA
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BSSD reduction to ISA
ISA task!
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BSSD Results
Database:
Convolved mixture Performance Estimation
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Post nonlinear ISA
Has it any sense?
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Post nonlinear ISA
Separability theorem:
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Post nonlinear ISA, results
Original
Observed
Nonlinear functions Hinton diagram Estimated functions
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ISA for facial components
In our database we had 800 different front view faces with the 6 basic facial expressions. We had thus 4,800 images in total. All images were sized to 40 £ 40 pixel.
A large 4800 £ 1600 data matrix was compiled; rows of this matrix were 1600 dimensional vectors formed by the pixel values of the individual images. The columns of this matrix were considered as mixed signals.
The observed 4800 dimensional signals were compressed by PCA to 60 dimensions and we searched for 4 pieces of ISA subspaces.
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ISA for facial components
Estimated subspaces
mouth
eye brushes
eyes
facial profiles
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Ongoing Projects & Future Plans
Multilinear (tenzorial) ISA BSSD in the Fourier domain
EEG, fMRI data processing
Low-dimensional embedding with ICA / ISA constraints Low-dimensional embedding of time series
Variational Bayesian Hidden Markov Factor Analyzer
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Thanks for your attention!
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References
Independent Process Analysis without Combinatorial Efforts. Z. Szabó, B. Póczos and A. Lőrincz. (ICA2007, accepted)
Post Nonlinear Independent Subspace Analysis.Z. Szabó, B. Póczos, G. Szirtes and A. Lőrincz. 2007. (ICANN 2007, accepted)
Undercomplete BSSD via Linear Prediction. Z. Szabó, B. Póczos and A. Lőrincz. 2007. (ECML 2007, accepted)
Undercomplete Blind Subspace Deconvolution Z. Szabó, B. Póczos and A. Lőrincz. 2007.Journal of Machine Learning Research. vol. 8, pp. 1063-1095.
Independent subspace analysis using geodesic spanning trees B. Póczos and A. LőrinczProc. of ICML 2005, Bonn, ICML: 673-680
Cross-Entropy Optimization for Independent Process AnalysisZ. Szabó, B. Póczos and A. LőrinczProc. of ICA 2006, Charleston, SC: LNCS 3889, 909-916, Springer Verlag
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References
Noncombinatorial estimation of independent auto-regressive sourcesB. Póczos and A. Lőrincz 2005.Neurocomputing vol. 69, pp. 2416-2419
Independent Subspace Analysis on Innovations B. Póczos, B. Takács and A. LőrinczProc. of. ECML 2005, Porto, LNAI 3720: 698-706, Springer-Verlag
Independent subspace analysis using k-nearest neighborhood distances B. Póczos and A. LőrinczProc. of ICANN 2005, Warsaw, LNCS 3697: 163-168, Springer-Verlag
Separation theorem for independent subspace analysis with sufficient conditions.Z. Szabó, B. Póczos and A. Lőrincz.Technical report, Eötvös Loránd University, Budapest.
Cost component analysisAndrás Lőrincz and Barnabás Póczos 2003.International Journal of Neural Systems, Vol. 13, pp. 183-192.
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