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|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|
|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
|publisher version/DOI: ||http://dx.doi.org/10.1016/j.neucom.2007.09.017|
|appears in collections||DETI - Artigos|
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