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 proceedingConference contributionpeer-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 languageEnglish
Title of host publicationIndependent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings
Pages254-261
Number of pages8
DOIs
StatePublished - 2006
Externally publishedYes
Event6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006 - Charleston, SC, United States
Duration: 5 Mar 20068 Mar 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3889 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006
Country/TerritoryUnited States
CityCharleston, SC
Period5/03/068/03/06

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