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 proceedingConference 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 languageEnglish (US)
Title of host publicationBioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings
PublisherSpringer Verlag
Pages477-487
Number of pages11
ISBN (Print)3540720308, 9783540720300
DOIs
StatePublished - 2007
Externally publishedYes
Event3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007 - Atlanta, GA, United States
Duration: May 7 2007May 10 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4463 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007
Country/TerritoryUnited States
CityAtlanta, GA
Period5/7/075/10/07

Keywords

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

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