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Conference Paper

Nonlinear blind source separation using kernel feature spaces

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Citation

Harmeling, S., Ziehe, A., Kawanabe, M., Blankertz, B., & Müller, K.-R. (2001). Nonlinear blind source separation using kernel feature spaces. In T.-W. Lee, T. Jung, S. Makeig, & T. Sejnowski (Eds.), Third International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2001) (pp. 102-107).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E164-E
Abstract
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.