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

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

4 Zitate (Scopus)

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.

OriginalspracheEnglisch
TitelIndependent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings
Seiten254-261
Seitenumfang8
DOIs
PublikationsstatusVeröffentlicht - 2006
Extern publiziertJa
Veranstaltung6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006 - Charleston, SC, USA/Vereinigte Staaten
Dauer: 5 März 20068 März 2006

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band3889 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006
Land/GebietUSA/Vereinigte Staaten
OrtCharleston, SC
Zeitraum5/03/068/03/06

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