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Nonlinear blind source separation using kernel feature spaces

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83954

Harmeling,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84096

Müller,  K-R
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Harmeling, S., Ziehe A, Kawanabe M, Blankertz, B., & Müller, K.-R. (2001). Nonlinear blind source separation using kernel feature spaces. Proceedings of the Third International Workshop on Independent Component Analysis and Blind Signal Separation (ICA 2001), 102-107.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-E164-E
Zusammenfassung
In this work we propose a kernel-based blind source separation (BSS) algorithm that can perform nonlinear BSS for general invertible nonlinearities. For our kTDSEP algorithm we have to go through four steps: (i) adapting to the intrinsic dimension of the data mapped to feature space F, (ii) finding an orthonormal basis of this submanifold, (iii) mapping the data into the subspace of F spanned by this orthonormal basis, and (iv) applying temporal decorrelation BSS (TDSEP) to the mapped data. After demixing we get a number of irrelevant components and the original sources. To find out which ones are the components of interest, we propose a criterion that allows to identify the original sources. The excellent performance of kTDSEP is demonstrated in experiments on nonlinearly mixed speech data.