Blind Matrix Decomposition Via Genetic Optimization of Sparseness and Nonnegativity Constraints (original) (raw)

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 is discontinuous and possesses many local minima, we use a genetic algorithm for its minimization. Application to a microarray data set will be considered also.

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Authors and Affiliations

  1. Institute of Biophysics, University of Regensburg, 93040 Regensburg, Germany
    Kurt Stadlthanner, Fabian J. Theis & Elmar W. Lang
  2. DETI / IEETA, Universidade de Aveiro, 3810-Aveiro, Portugal
    Ana Maria Tomé
  3. DATC, Universidad de Granada, E-18071 Granada, Spain
    Carlos G. Puntonet

Authors

  1. Kurt Stadlthanner
  2. Fabian J. Theis
  3. Elmar W. Lang
  4. Ana Maria Tomé
  5. Carlos G. Puntonet

Editor information

Joaquim Marques de Sá Luís A. Alexandre Włodzisław Duch Danilo Mandic

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© 2007 Springer-Verlag Berlin Heidelberg

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Stadlthanner, K., Theis, F.J., Lang, E.W., Tomé, A.M., Puntonet, C.G. (2007). Blind Matrix Decomposition Via Genetic Optimization of Sparseness and Nonnegativity Constraints. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4\_81

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