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Separation of post-nonlinear mixtures using ACE and temporal decorrelation

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

Kawanabe M, 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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Citation

Ziehe, A., Kawanabe M, Harmeling, S., & Müller, K.-R. (2001). Separation of post-nonlinear mixtures using ACE and temporal decorrelation. Proceedings of the Third International Workshop on Independent Component Analysis and Blind Signal Separation (ICA 2001), 433-438.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E169-4
Abstract
We propose an efficient method based on the concept of maximal correlation that reduces the post-nonlinear blind source separation problem (PNL BSS) to a linear BSS problem. For this we apply the Alternating Conditional Expectation (ACE) algorithm – a powerful technique from nonparametric statistics – to approximately invert the (post-)nonlinear functions. Interestingly, in the framework of the ACE method convergence can be proven and in the PNL BSS scenario the optimal transformation found by ACE will coincide with the desired inverse functions. After the nonlinearities have been removed by ACE, temporal decorrelation (TD) allows us to recover the source signals. An excellent performance underlines the validity of our approach and demonstrates the ACE-TD method on realistic examples.