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Sparse Nonnegative Matrix Factorization Applied to Microarray Data Sets

Stadlthanner, K. ; Theis, Fabian J. ; Lang, Elmar ; Tomé, A. ; Puntonet, C. G. ; Gomez-Vilda, P. ; Langmann, T. ; Schmitz, G.



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. ...

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