Hybridizing sparse component analysis with genetic algorithms for blind source separation

Kurt Stadlthanner,Fabian J. Theis,Carlos G. Puntonet,Juan M. Górriz,Ana Maria Tomé,Elmar W. Lang

ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis（2005）

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摘要

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 nonnegative Blind Source Separation (BSS) problems. In this paper we present first results of an extension to the NMF algorithm which solves the BSS problem when the underlying sources are sufficiently sparse. As the proposed target function has many local minima, we use a genetic algorithm for its minimization.

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关键词

nonnegative matrix factorization,bss problem,nonnegative multivariate data,genetic algorithm,unique result,nmf algorithm,sparse component analysis,blind source,underlying source,blind source separation,proposed target function,local minimum

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