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

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

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

Publisher link

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

Find It

Find It at U of M Libraries

Cite this

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)).

@inproceedings{0e57b48eab074db49b219a0788c1a754,

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

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.",

keywords = "Cancer class discovery, Convergence criterion, Non-negative matrix factorization, Non-negativity-constrained least squares",

author = "Hyunsoo Kim and Haesun Park",

year = "2007",

doi = "10.1007/978-3-540-72031-7\_43",

language = "English (US)",

isbn = "3540720308",

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

publisher = "Springer Verlag",

pages = "477--487",

booktitle = "Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings",

note = "3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007 ; Conference date: 07-05-2007 Through 10-05-2007",

}

TY - GEN

T1 - Cancer class discovery using non-negative matrix factorization based on alternating non-negativity-constrained least squares

AU - Kim, Hyunsoo

AU - Park, Haesun

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

KW - Cancer class discovery

KW - Convergence criterion

KW - Non-negative matrix factorization

KW - Non-negativity-constrained least squares

UR - https://www.scopus.com/pages/publications/34547453725

UR - https://www.scopus.com/pages/publications/34547453725#tab=citedBy

U2 - 10.1007/978-3-540-72031-7_43

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

M3 - Conference contribution

AN - SCOPUS:34547453725

SN - 3540720308

SN - 9783540720300

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

SP - 477

EP - 487

BT - Bioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings

PB - Springer Verlag

T2 - 3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007

Y2 - 7 May 2007 through 10 May 2007

ER -

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this