Hybridizing sparse component analysis with genetic algorithms for microarray analysis

Authors:
K. Stadlthanner;F. J. Theis;E. W. Lang;A. M. Tomé;C. G. Puntonet;J. M. Górriz
Affiliations:
Institute of Biophysics, Computational Intelligence Group, University of Regensburg, Eupener Str. 114, 52066 Aachen, Germany;Institute of Biophysics, Computational Intelligence Group, University of Regensburg, Eupener Str. 114, 52066 Aachen, Germany;Institute of Biophysics, Computational Intelligence Group, University of Regensburg, Eupener Str. 114, 52066 Aachen, Germany;Dept. de Electrónica e Telecomunicaçíes/IEETA, Universidade de Aveiro, 3810 Aveiro, Portugal;Dept. Arqitectura y Tecnología de Computadores, Universidad de Granada, 18371 Granada, Spain;Dept. Arqitectura y Tecnología de Computadores, Universidad de Granada, 18371 Granada, Spain
Venue:
Neurocomputing
Year:
2008

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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 blind source separation (BSS) problems. In this paper we present an extension of NMF capable of solving the BSS problem when the underlying sources are sufficiently sparse. In contrast to most well-established BSS methods, the devised algorithm is capable of solving the BSS problem in cases where the underlying sources are not independent or uncorrelated. As the proposed fitness function is discontinuous and possesses many local minima, we use a genetic algorithm for its minimization. Finally, we apply the devised algorithm to real world microarray data.