Zero-Entropy minimization for blind extraction of bounded sources (BEBS)

Authors:
Frédéric Vrins;Deniz Erdogmus;Christian Jutten;Michel Verleysen
Affiliations:
Machine Learning Group, Université catholique de Louvain, Louvain-la-Neuve, Belgium;Dep. of CSEE, OGI Oregon, Health and Science University, Portland, Oregon;Laboratoire des Images et des Signaux, Institut National Polytechnique de Grenoble (INPG), France;Machine Learning Group, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

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Abstract

Renyi’s entropy can be used as a cost function for blind source separation (BSS). Previous works have emphasized the advantage of setting Renyi’s exponent to a value different from one in the context of BSS. In this paper, we focus on zero-order Renyi’s entropy minimization for the blind extraction of bounded sources (BEBS). We point out the advantage of choosing the extended zero-order Renyi’s entropy as a cost function in the context of BEBS, when the sources have non-convex supports.