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Sparse Nonnegative Matrix Factorization Applied to Microarray Data Sets

Stadlthanner, K. and Theis, Fabian J. and Lang, Elmar and Tomé, A. and Puntonet, C. G. and Gomez-Vilda, P. and Langmann, T. and Schmitz, G. (2006) Sparse Nonnegative Matrix Factorization Applied to Microarray Data Sets. In: Rosca, J., (ed.) Independent Component Analysis and Blind Signal Separation, 6th International Conference, ICA 2006, Charleston, SC, USA, March 5-8, 2006. Proceedings. Lecture notes in computer science, 3889. Springer, Berlin, pp. 254-261. ISBN 3-540-32630-8 (print), 978-3-540-32630-4 (e-book).

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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. ...

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Item Type:Book Section
Date:2006
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Identification Number:
ValueType
10.1007/11679363_32DOI
Subjects:500 Science > 570 Life sciences
Status:Published
Refereed:Unknown
Created at the University of Regensburg:Unknown
Owner: Gertraud Kellers
Deposited On:01 Oct 2010 08:01
Last Modified:01 Oct 2010 08:01
Item ID:16867
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