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An improved geometric overcomplete blind source separation algorithm

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Artificial Neural Nets Problem Solving Methods (IWANN 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2687))

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Abstract

In this paper, we generalize the efficient geometric ICA algorithm FastGeo to overcomplete settings with more sources than sensors. The solution to this underdetermined problem will be presented in a two step approach. With geometric ICA we get an efficient method for the step—matrix-recovery—while the second step—source-recovery— uses a maximum-likelihood approach.

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Author information

Authors and Affiliations

  1. Institute of Biophysics, AG Neuro- and Bioinformatics, University of Regensburg, D-93040, Regensburg, Germany

    Fabian J. Theis & Elmar W. Lang

  2. Dept. Arquitecura y Tecnologia de Computadores, Escuela Tcnica Superior de Ingenieria Informatica, Universidad de Granada, E-18071, Granada, Spain

    Carlos G. Puntonet

Authors
  1. Fabian J. Theis
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  2. Carlos G. Puntonet
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  3. Elmar W. Lang
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Editor information

Editors and Affiliations

  1. E.T.S. de Ingeniería Informática Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia, Juan del Rosal, 16, 28040, Madrid, Spain

    José Mira  & José R. Álvarez  & 

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© 2003 Springer-Verlag Berlin Heidelberg

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Theis, F.J., Puntonet, C.G., Lang, E.W. (2003). An improved geometric overcomplete blind source separation algorithm. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_34

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  • DOI: https://doi.org/10.1007/3-540-44869-1_34

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40211-4

  • Online ISBN: 978-3-540-44869-3

  • eBook Packages: Springer Book Archive

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