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Hybridizing Sparse Component Analysis with Genetic Algorithms for Blind Source Separation

  • Kurt Stadlthanner
  • Fabian J. Theis
  • Carlos G. Puntonet
  • Juan M. Górriz
  • Ana Maria Tomé
  • Elmar W. Lang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3745)

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.

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References

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    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 40, 788–791 (1999)Google Scholar
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    Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research 5, 1457–1469 (2004)MathSciNetGoogle Scholar
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    Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neuronal Networks 10(3), 626–634 (1999)CrossRefGoogle Scholar
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    Chipperfield, A., Fleming, P., Pohlheim, H., Fonseca, C.: Genetic Algorithm Toolbox, Evolutionary Computation Research Group, University fo Sheffield, www.shef.ac.uk/acse/research/ecrg/

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kurt Stadlthanner
    • 1
  • Fabian J. Theis
    • 1
  • Carlos G. Puntonet
    • 2
  • Juan M. Górriz
    • 2
  • Ana Maria Tomé
    • 3
  • Elmar W. Lang
    • 1
  1. 1.Institute of BiophysicsUniversity of RegensburgRegensburgGermany
  2. 2.Dept. Arquitectura y Tecnología de ComputadoresUniversidad de GranadaGranadaSpain
  3. 3.Dept. de Electrónica e Telecomunicações / IEETAUniversidade de AveiroAveiroPortugal

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