Hybridizing sparse component analysis with genetic algorithms for microarray analysis

Nonnegative matrix factorization (NMF) has proven to be a useful tool for the previous termanalysisnext term 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 previous termsparse.next term In contrast to most well-established BSS methods, the devised previous termalgorithmnext term 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 previous termgenetic algorithmnext term for its minimization. Finally, we apply the devised previous termalgorithmnext term to real world previous termmicroarraynext term data