Sparse nonnegative matrix factorization applied to microarray data sets

K. Stadlthanner; F. J. Theis; E. W. Lang; A. M. Tomé; C. G. Puntonet; P. Gómez Vilda; T. Langmann; G. Schmitz

doi:10.1007/11679363_32

Sparse nonnegative matrix factorization applied to microarray data sets

K. Stadlthanner, F. J. Theis, E. W. Lang, A. M. Tomé, C. G. Puntonet, P. Gómez Vilda, T. Langmann, G. Schmitz

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

4 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 has many local minima, we use a genetic algorithm for its minimization.

Original language	English
Title of host publication	Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings
Pages	254-261
Number of pages	8
DOIs	https://doi.org/10.1007/11679363_32
State	Published - 2006
Externally published	Yes
Event	6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006 - Charleston, SC, United States Duration: 5 Mar 2006 → 8 Mar 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3889 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006
Country/Territory	United States
City	Charleston, SC
Period	5/03/06 → 8/03/06

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10.1007/11679363_32

Cite this

Stadlthanner, K., Theis, F. J., Lang, E. W., Tomé, A. M., Puntonet, C. G., Vilda, P. G., Langmann, T., & Schmitz, G. (2006). Sparse nonnegative matrix factorization applied to microarray data sets. In Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings (pp. 254-261). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3889 LNCS). https://doi.org/10.1007/11679363_32

Stadlthanner, K. ; Theis, F. J. ; Lang, E. W. et al. / Sparse nonnegative matrix factorization applied to microarray data sets. Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings. 2006. pp. 254-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{964c3694e416470dbb06a797cad0d718,

title = "Sparse nonnegative matrix factorization applied to microarray data sets",

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.",

author = "K. Stadlthanner and Theis, {F. J.} and Lang, {E. W.} and Tom{\'e}, {A. M.} and Puntonet, {C. G.} and Vilda, {P. G{\'o}mez} and T. Langmann and G. Schmitz",

year = "2006",

doi = "10.1007/11679363_32",

language = "English",

isbn = "3540326308",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "254--261",

booktitle = "Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings",

note = "6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006 ; Conference date: 05-03-2006 Through 08-03-2006",

}

Stadlthanner, K, Theis, FJ, Lang, EW, Tomé, AM, Puntonet, CG, Vilda, PG, Langmann, T & Schmitz, G 2006, Sparse nonnegative matrix factorization applied to microarray data sets. in Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3889 LNCS, pp. 254-261, 6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006, Charleston, SC, United States, 5/03/06. https://doi.org/10.1007/11679363_32

Sparse nonnegative matrix factorization applied to microarray data sets. / Stadlthanner, K.; Theis, F. J.; Lang, E. W. et al.
Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings. 2006. p. 254-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3889 LNCS).

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

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T1 - Sparse nonnegative matrix factorization applied to microarray data sets

AU - Stadlthanner, K.

AU - Theis, F. J.

AU - Lang, E. W.

AU - Tomé, A. M.

AU - Puntonet, C. G.

AU - Vilda, P. Gómez

AU - Langmann, T.

AU - Schmitz, G.

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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 has many local minima, we use a genetic algorithm for its minimization.

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DO - 10.1007/11679363_32

M3 - Conference contribution

AN - SCOPUS:33745711532

SN - 3540326308

SN - 9783540326304

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Stadlthanner K, Theis FJ, Lang EW, Tomé AM, Puntonet CG, Vilda PG et al. Sparse nonnegative matrix factorization applied to microarray data sets. In Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings. 2006. p. 254-261. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11679363_32

Sparse nonnegative matrix factorization applied to microarray data sets

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