Equi-convergence Algorithm for Blind Separation of Sources with Arbitrary Distributions

Authors:
Liqing Zhang;Shun-ichi Amari;Andrzej Cichocki
Affiliations:
-;-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Year:
2001

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Abstract

This paper presents practical implements of the equi-convergent learning algorithm for blind source separation. The equiconvergent algorithm [4] has favorite properties such as isotropic convergence and universal convergence, but it requires to estimate unknown activation functions and certain unknown statistics of source signals. The estimation of such activation functions and statistics becomes critical in realizing the equi-convergent algorithm. It is the purpose of this paper to develop a new approach to estimate the activation functions adaptively for blind source separation. We propose the exponential type family as a model for probability density functions. A method of constructing an exponential family from the activation (score) functions is proposed. Then, a learning rule based on the meximum likelihood is derived to update the parameters in the exponential family. The learning rule is compatible with minimization of mutual information for training demixing models. Finally, computer simulations are given to demonstrate the effectiveness and validity of the proposed approach.