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.
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Vrins, F., Erdogmus, D., Jutten, C., Verleysen, M. (2006). Zero-Entropy Minimization for Blind Extraction of Bounded Sources (BEBS). In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_93
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DOI: https://doi.org/10.1007/11679363_93
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