Sparse nonnegative matrix factorization applied to microarray data sets

Authors:
K. Stadlthanner;F. J. Theis;E. W. Lang;A. M. Tomé;C. G. Puntonet;P. Gómez Vilda;T. Langmann;G. Schmitz
Affiliations:
Institute of Biophysics, University of Regensburg, Regensburg, Germany;Institute of Biophysics, University of Regensburg, Regensburg, Germany;Institute of Biophysics, University of Regensburg, Regensburg, Germany;DET / IEETA, Universidade de Aveiro, Aveiro, Portugal;DATC, Universidad de Granada, Granada, Spain;DATSI, Universidad Politécnica de Madrid, Madrid, Spain;Institute for Clinical Chemistry and Laboratory Medicine, University Hospital, Regensburg, Germany;Institute for Clinical Chemistry and Laboratory Medicine, University Hospital, Regensburg, Germany
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

Citing 2
Cited 2

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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.