Blind separation of post-nonlinear mixtures using gaussianizing transformations 
and temporal decorrelation

Ziehe, A; Kawanabe M, Harmeling, S; Müller, K-R; Amari,; S.-I.,; Cichocki, A.; Makino, S.; Murata, N.

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Blind separation of post-nonlinear mixtures using gaussianizing transformations and temporal decorrelation

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

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

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引用

Ziehe, A., Kawanabe M, Harmeling, S., & Müller, K.-R. (2003). Blind separation of post-nonlinear mixtures using gaussianizing transformations and temporal decorrelation. Proceedings of the 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA 2003), 269-274.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-DCB5-E

要旨

At the previous workshop (ICA2001) we proposed the ACE-TD method that reduces the post-nonlinear blind source separation problem (PNL BSS) to a linear BSS problem. The method utilizes the Alternating Conditional Expectation (ACE) algorithm to approximately invert the (post-)non-linear functions. In this contribution, we propose an alternative procedure called Gaussianizing transformation, which is motivated by the fact that linearly mixed signals before nonlinear transformation are approximately Gaussian distributed. This heuristic, but simple and efficient procedure yields similar results as the ACE method and can thus be used as a fast and effective equalization method. After equalizing the nonlinearities, temporal decorrelation separation (TDSEP) allows us to recover the source signals. Numerical simulations on realistic examples are performed to compare "Gauss-TD" with "ACE-TD".