Nonlinear blind source separation using kernel feature spaces

Harmeling, S; Ziehe, A; Kawanabe, M; Blankertz, B; Müller, K-R

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Nonlinear blind source separation using kernel feature spaces

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http://doc.ml.tu-berlin.de/publications/publications/HarZieKawBlaMue01.pdf
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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: https://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.