Nonlinear blind source separation using kernel feature spaces
2001
Conference Paper
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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.
Author(s): | Harmeling, S. and Ziehe, A. and Kawanabe, M. and Blankertz, B. and Müller, K-R. |
Book Title: | ICA 2001 |
Journal: | Proceedings of the Third International Workshop on Independent Component Analysis and Blind Signal Separation (ICA 2001) |
Pages: | 102-107 |
Year: | 2001 |
Month: | December |
Day: | 0 |
Editors: | Lee, T.-W. , T.P. Jung, S. Makeig, T. J. Sejnowski |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | Third International Workshop on Independent Component Analysis and Blind Signal Separation |
Event Place: | San Diego, CA, USA |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
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BibTex @inproceedings{6364, title = {Nonlinear blind source separation using kernel feature spaces}, author = {Harmeling, S. and Ziehe, A. and Kawanabe, M. and Blankertz, B. and M{\"u}ller, K-R.}, journal = {Proceedings of the Third International Workshop on Independent Component Analysis and Blind Signal Separation (ICA 2001)}, booktitle = {ICA 2001}, pages = {102-107}, editors = {Lee, T.-W. , T.P. Jung, S. Makeig, T. J. Sejnowski}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = dec, year = {2001}, doi = {}, month_numeric = {12} } |