Hybridizing sparse component analysis with genetic algorithms for blind source separation

Authors:
Kurt Stadlthanner;Fabian J. Theis;Carlos G. Puntonet;Juan M. Górriz;Ana Maria Tomé;Elmar W. Lang
Affiliations:
Institute of Biophysics, University of Regensburg, Regensburg, Germany;Institute of Biophysics, University of Regensburg, Regensburg, Germany;Dept. Arquitectura y Tecnología de Computadores, Universidad de Granada, Granada, Spain;Dept. Arquitectura y Tecnología de Computadores, Universidad de Granada, Granada, Spain;Dept. de Electrónica e Telecomunicações / IEETA, Universidade de Aveiro, Aveiro, Portugal;Institute of Biophysics, University of Regensburg, Regensburg, Germany
Venue:
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Year:
2005

Citing 2
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Abstract

Nonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. However, it is known not to lead to unique results when applied to nonnegative Blind Source Separation (BSS) problems. In this paper we present first results of an extension to the NMF algorithm which solves the BSS problem when the underlying sources are sufficiently sparse. As the proposed target function has many local minima, we use a genetic algorithm for its minimization.