Hybridizing sparse component analysis with genetic algorithms for microarray an...

• Autor(es): Stadlthanner, K. ; Theis, F. J. ; Lang, E. W. ; Tomé, A. M. ; Puntonet, C. G. ; Górriz, J. M.
• Data: 2008
• Identificador Persistente: http://hdl.handle.net/10773/5819
• Origem: RIA - Repositório Institucional da Universidade de Aveiro
• Assunto(s): Sparse nonnegative matrix factorization; Blind source separation; Gene microarray analysis
• Tipo de Documento: Artigo
• Idioma: Inglês

Descrição

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. Siemens AG, Corporate Technology, Munich - Biomarker DFG - GRK 638: Nonlinearity and Nonequilibrium in Condensed Matter DAAD-GRICES Acções Integradas Luso-Alemãs - GEVD-MP DAAD Acciones Integradas Hispano-Alemanas - Microarrays