Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/5819
Title: | Hybridizing sparse component analysis with genetic algorithms for microarray analysis |
Author: | Stadlthanner, K. Theis, F. J. Lang, E. W. Tomé, A. M. Puntonet, C. G. Górriz, J. M. |
Keywords: | Sparse nonnegative matrix factorization Blind source separation Gene microarray analysis |
Issue Date: | Jun-2008 |
Publisher: | Elsevier |
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. |
Peer review: | yes |
URI: | http://hdl.handle.net/10773/5819 |
DOI: | 10.1016/j.neucom.2007.09.017 |
ISSN: | 0925-2312 |
Appears in Collections: | DETI - Artigos |
Files in This Item:
File | Description | Size | Format | |
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Kurt2008.pdf | 1.68 MB | Adobe PDF | View/Open |
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