Blind matrix decomposition via genetic optimization of sparseness and nonnegativity constraints

Kurt Stadlthanner; Fabian J. Theis; Elmar W. Lang; Ana Maria Tomé; Carlos G. Puntonet

doi:10.1007/978-3-540-74690-4_81

Blind matrix decomposition via genetic optimization of sparseness and nonnegativity constraints

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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 is discontinuous and possesses many local minima, we use a genetic algorithm for its minimization. Application to a microarray data set will be considered also.

Original language	English
Title of host publication	Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings
Publisher	Springer Verlag
Pages	799-808
Number of pages	10
Edition	PART 1
ISBN (Print)	9783540746898
DOIs	https://doi.org/10.1007/978-3-540-74690-4_81
State	Published - 2007
Externally published	Yes
Event	17th International Conference on Artificial Neural Networks, ICANN 2007 - Porto, Portugal Duration: 9 Sep 2007 → 13 Sep 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	4668 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Artificial Neural Networks, ICANN 2007
Country/Territory	Portugal
City	Porto
Period	9/09/07 → 13/09/07

Access to Document

10.1007/978-3-540-74690-4_81

Cite this

Stadlthanner, K., Theis, F. J., Lang, E. W., Tomé, A. M., & Puntonet, C. G. (2007). Blind matrix decomposition via genetic optimization of sparseness and nonnegativity constraints. In Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings (PART 1 ed., pp. 799-808). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4668 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-540-74690-4_81

Stadlthanner, Kurt ; Theis, Fabian J. ; Lang, Elmar W. et al. / Blind matrix decomposition via genetic optimization of sparseness and nonnegativity constraints. Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 1. ed. Springer Verlag, 2007. pp. 799-808 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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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 is discontinuous and possesses many local minima, we use a genetic algorithm for its minimization. Application to a microarray data set will be considered also.",

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Stadlthanner, K, Theis, FJ, Lang, EW, Tomé, AM & Puntonet, CG 2007, Blind matrix decomposition via genetic optimization of sparseness and nonnegativity constraints. in Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 4668 LNCS, Springer Verlag, pp. 799-808, 17th International Conference on Artificial Neural Networks, ICANN 2007, Porto, Portugal, 9/09/07. https://doi.org/10.1007/978-3-540-74690-4_81

Blind matrix decomposition via genetic optimization of sparseness and nonnegativity constraints. / Stadlthanner, Kurt; Theis, Fabian J.; Lang, Elmar W. et al.
Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 1. ed. Springer Verlag, 2007. p. 799-808 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4668 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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 is discontinuous and possesses many local minima, we use a genetic algorithm for its minimization. Application to a microarray data set will be considered also.

AB - 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 is discontinuous and possesses many local minima, we use a genetic algorithm for its minimization. Application to a microarray data set will be considered also.

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Stadlthanner K, Theis FJ, Lang EW, Tomé AM, Puntonet CG. Blind matrix decomposition via genetic optimization of sparseness and nonnegativity constraints. In Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 1 ed. Springer Verlag. 2007. p. 799-808. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-540-74690-4_81

Blind matrix decomposition via genetic optimization of sparseness and nonnegativity constraints

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