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Hybridizing Sparse Component Analysis with Genetic Algorithms for Blind Source Separation

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Biological and Medical Data Analysis (ISBMDA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3745))

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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.

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References

  1. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 40, 788–791 (1999)

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  4. 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/

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Author information

Authors and Affiliations

  1. Institute of Biophysics, University of Regensburg, 93040, Regensburg, Germany

    Kurt Stadlthanner, Fabian J. Theis & Elmar W. Lang

  2. Dept. Arquitectura y Tecnología de Computadores, Universidad de Granada, E-18071, Granada, Spain

    Carlos G. Puntonet & Juan M. Górriz

  3. Dept. de Electrónica e Telecomunicações / IEETA, Universidade de Aveiro, 3810, Aveiro, Portugal

    Ana Maria Tomé

Authors
  1. Kurt Stadlthanner
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  2. Fabian J. Theis
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  3. Carlos G. Puntonet
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  4. Juan M. Górriz
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  5. Ana Maria Tomé
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  6. Elmar W. Lang
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Editor information

Editors and Affiliations

  1. University of Aveiro, DETI/IEETA, 3810-193, Aveiro, Portugal

    José Luís Oliveira

  2. Biomedical Informatics Group, Dep. Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Spain

    Víctor Maojo

  3. Medical Bioinformatics Department, Institute of Health ‘Carlos III’, Ctra. Majadahonda-Pozuelo, km 2. 28220 Majadahonda, Madrid

    Fernando Martín-Sánchez

  4. Department of Electronics and Telecommunications (DET/IEETA), University of Aveiro, 3810 193, Aveiro, Portugal

    António Sousa Pereira

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© 2005 Springer-Verlag Berlin Heidelberg

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Stadlthanner, K., Theis, F.J., Puntonet, C.G., Górriz, J.M., Tomé, A.M., Lang, E.W. (2005). Hybridizing Sparse Component Analysis with Genetic Algorithms for Blind Source Separation. In: Oliveira, J.L., Maojo, V., Martín-Sánchez, F., Pereira, A.S. (eds) Biological and Medical Data Analysis. ISBMDA 2005. Lecture Notes in Computer Science(), vol 3745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573067_15

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  • DOI: https://doi.org/10.1007/11573067_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29674-4

  • Online ISBN: 978-3-540-31658-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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