Stadlthanner, K. and Theis, F. J. and Lang, E. W. and Tome, A. M. and Puntonet, C. G. and Gorriz, J. M. (2008) Hybridizing sparse component analysis with genetic algorithms for microarray analysis. NEUROCOMPUTING, 71 (10-12). pp. 2356-2376. ISSN 0925-2312, 1872-8286
Full text not available from this repository. (Request a copy)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 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. (C) 2008 Elsevier B.V. All rights reserved.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | NONNEGATIVE MATRIX FACTORIZATION; BLIND SOURCE SEPARATION; ICA; sparse nonnegative matrix factorization; blind source separation; gene microarray analysis |
| Subjects: | 500 Science > 570 Life sciences |
| Divisions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 02 Nov 2020 07:13 |
| Last Modified: | 02 Nov 2020 07:13 |
| URI: | https://pred.uni-regensburg.de/id/eprint/30846 |
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