On optimal and universal nonlinearities for blind signal separation

Authors:
H. Mathis;S. C. Douglas
Affiliations:
Signal & Inf. Process. Lab., Swiss Fed. Inst. of Technol., Zurich, Switzerland;-
Venue:
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Year:
2001

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A Distortion-Free Learning Algorithm for Feedforward Multi-Channel Blind Source Separation

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

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Abstract

The search for universally applicable nonlinearities in blind signal separation has produced nonlinearities that are optimal for a given distribution, as well as nonlinearities that are most robust against model mismatch. This paper shows yet another justification for the score function, which is in some sense a very robust nonlinearity. It also shows that among the class of parameterizable nonlinearities, the threshold nonlinearity with the threshold as a parameter is able to separate any non-Gaussian distribution, a fact that is also proven in this paper.