Sparse robust matrix tri-factorization with application to cancer genomics

Seung Jun Kim, TaeHyun Hwang, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Nonnegative matrix tri-factorization (NMTF) X ≈ FSG T with all matrices nonnegative can reveal simultaneous row and column clusters of X, as well as the associations among the two. In this work, a sparsity-promoting variant is proposed and a simple multiplicative algorithm is developed. The resulting sparse NMTF is further robustified to cope with presence of outliers in the data. A synthetic example illustrates the efficacy of the method. A novel application to cancer patient clustering and pathway analysis is presented using real datasets.

Original languageEnglish (US)
Title of host publication2012 3rd International Workshop on Cognitive Information Processing, CIP 2012
DOIs
StatePublished - Aug 13 2012
Event2012 3rd International Workshop on Cognitive Information Processing, CIP 2012 - Baiona, Spain
Duration: May 28 2012May 30 2012

Publication series

Name2012 3rd International Workshop on Cognitive Information Processing, CIP 2012

Other

Other2012 3rd International Workshop on Cognitive Information Processing, CIP 2012
CountrySpain
CityBaiona
Period5/28/125/30/12

ASJC Scopus subject areas

  • Information Systems

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