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 Hybridizing sparse component analysis with genetic algorithms for microarray analysis
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/5819

title: Hybridizing sparse component analysis with genetic algorithms for microarray analysis
authors: Stadlthanner, K.
Theis, F. J.
Lang, E. W.
Tomé, A. M.
Puntonet, C. G.
Górriz, J. M.
keywords: Sparse nonnegative matrix factorization
Blind source separation
Gene microarray analysis
issue date: Jun-2008
publisher: Elsevier
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.
URI: http://hdl.handle.net/10773/5819
ISSN: 0925-2312
publisher version/DOI: http://dx.doi.org/10.1016/j.neucom.2007.09.017
source: Neurocomputing
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