Cancer class discovery using non-negative matrix factorization based on alternating non-negativity-constrained least squares

Hyunsoo Kim; Haesun Park

doi:10.1007/978-3-540-72031-7_43

Cancer class discovery using non-negative matrix factorization based on alternating non-negativity-constrained least squares

Hyunsoo Kim, Haesun Park

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

6 Scopus citations

Abstract

Many bioinformatics problems deal with chemical concentrations that should be non-negative. Non-negative matrix factorization (NMF) is an approach to take advantage of non-negativity in data. We have recently developed sparse NMF algorithms via alternating nonnegativity-constrained least squares in order to obtain sparser basis vectors or sparser mixing coefficients for each sample, which lead to easier interpretation. However, the additional sparsity constraints are not always required. In this paper, we conduct cancer class discovery using NMF based on alternating non-negativity-constrained least squares (NMF/ANLS) without any additional sparsity constraints after introducing a rigorous convergence criterion for biological data analysis.

Original language	English (US)
Title of host publication	Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings
Publisher	Springer Verlag
Pages	477-487
Number of pages	11
ISBN (Print)	3540720308, 9783540720300
DOIs	https://doi.org/10.1007/978-3-540-72031-7_43
State	Published - 2007
Externally published	Yes
Event	3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007 - Atlanta, GA, United States Duration: May 7 2007 → May 10 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4463 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007
Country/Territory	United States
City	Atlanta, GA
Period	5/7/07 → 5/10/07

Keywords

Cancer class discovery
Convergence criterion
Non-negative matrix factorization
Non-negativity-constrained least squares

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Publisher link

10.1007/978-3-540-72031-7_43

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Kim, H., & Park, H. (2007). Cancer class discovery using non-negative matrix factorization based on alternating non-negativity-constrained least squares. In Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings (pp. 477-487). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4463 LNBI). Springer Verlag. https://doi.org/10.1007/978-3-540-72031-7_43

Cancer class discovery using non-negative matrix factorization based on alternating non-negativity-constrained least squares. / Kim, Hyunsoo; Park, Haesun.
Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings. Springer Verlag, 2007. p. 477-487 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4463 LNBI).

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

Kim, H & Park, H 2007, Cancer class discovery using non-negative matrix factorization based on alternating non-negativity-constrained least squares. in Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4463 LNBI, Springer Verlag, pp. 477-487, 3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007, Atlanta, GA, United States, 5/7/07. https://doi.org/10.1007/978-3-540-72031-7_43

Kim H, Park H. Cancer class discovery using non-negative matrix factorization based on alternating non-negativity-constrained least squares. In Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings. Springer Verlag. 2007. p. 477-487. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-72031-7_43

Kim, Hyunsoo ; Park, Haesun. / Cancer class discovery using non-negative matrix factorization based on alternating non-negativity-constrained least squares. Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings. Springer Verlag, 2007. pp. 477-487 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Cancer class discovery using non-negative matrix factorization based on alternating non-negativity-constrained least squares

Abstract

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Keywords

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