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Independent Slow Feature Analysis and Nonlinear Blind Source Separation

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Independent Component Analysis and Blind Signal Separation (ICA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3195))

Abstract

We present independent slow feature analysis as a new method for nonlinear blind source separation. It circumvents the indeterminacy of nonlinear independent component analysis by combining the objectives of statistical independence and temporal slowness. The principle of temporal slowness is adopted from slow feature analysis, an unsupervised method to extract slowly varying features from a given observed vectorial signal. The performance of the algorithm is demonstrated on nonlinearly mixed speech data.

This work has been supported by the Volkswagen Foundation through a grant to LW for a junior research group.

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Author information

Authors and Affiliations

  1. Institute for Theoretical Biology, Humboldt University Berlin, Invalidenstraße 43, D-10115, Berlin, Germany

    Tobias Blaschke & Laurenz Wiskott

Authors
  1. Tobias Blaschke
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  2. Laurenz Wiskott
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Editor information

Editors and Affiliations

  1. Dept. of Architecture and Computer Technology, University of Granada, Spain

    Carlos G. Puntonet  & Alberto Prieto  & 

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Blaschke, T., Wiskott, L. (2004). Independent Slow Feature Analysis and Nonlinear Blind Source Separation. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_94

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  • DOI: https://doi.org/10.1007/978-3-540-30110-3_94

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

  • eBook Packages: Springer Book Archive

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