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Hybridizing sparse component analysis with genetic algorithms for microarray analysis

Stadlthanner, Kurt and Theis, Fabian J. and Tomé, A. M. and Puntonet, Carlos G. and Górriz, J. M. and Lang, Elmar (2008) Hybridizing sparse component analysis with genetic algorithms for microarray analysis. Neurocomputing 71 (10-12), pp. 2356-2376.

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Abstract

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 ...

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Item Type:Article
Date:2008
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Identification Number:
ValueType
10.1016/j.neucom.2007.09.017DOI
Subjects:500 Science > 570 Life sciences
Status:Published
Refereed:Unknown
Created at the University of Regensburg:Unknown
Owner: Gertraud Kellers
Deposited On:04 Oct 2010 09:43
Last Modified:04 Oct 2010 09:43
Item ID:16909
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