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Hybridizing sparse component analysis with genetic algorithms for blind source separation

Stadlthanner, K. and Theis, Fabian J. and Lang, Elmar and Puntonet, Carlos G. (2005) Hybridizing sparse component analysis with genetic algorithms for blind source separation. In: Oliveira, José Luis, (ed.) Biological and medical data analysis: 6th international symposium, ISBMDA 2005, Aveiro, Portugal, November 10 - 11, 2005 ; proceedings. Lecture notes in computer science: Lecture notes in bioinformatics, 3745. Springer, Berlin, pp. 137-148. ISBN 3-540-29674-3.

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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 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.

Item Type:Book Section
Institutions: Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Identification Number:
ValueType
10.1007/11573067_15DOI
Subjects:500 Science > 570 Life sciences
Status:Published
Refereed:Unknown
Created at the University of Regensburg:Unknown
Owner:Gertraud Kellers
Deposited On:15 Oct 2010 10:54
Last Modified:15 Oct 2010 10:54
Item ID:17319
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