Deutsch
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. As the proposed target function has many local minima, we use a genetic algorithm for its minimization.
| Item Type: | Book Section | ||||
|---|---|---|---|---|---|
| Institutions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang | ||||
| Identification Number: |
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| 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 10:01 | ||||
| Last Modified: | 01 Oct 2010 10:01 | ||||
| Item ID: | 16867 |
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