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International Conference on Independent Component Analysis and Signal Separation

ICA 2004: Independent Component Analysis and Blind Signal Separation pp 343–349Cite as

An Approach of Moment-Based Algorithm for Noisy ICA Models

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

Abstract

Factor analysis is well known technique to uncorrelate observed signals with Gaussina noises before ICA (Independent Component Analysis) algorithms are applied. However, factor analysis is not applicable when the number of source signals are more than that of Ledermann’s bound, and when the observations are contaminated by non-Gaussian noises. In this paper, an approach is proposed based on higher-order moments of signals and noises in order to overcome those constraints.

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References

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Author information

Authors and Affiliations

  1. Department of Electrical, Electronics, and Computer Engineering, Waseda University, 3-4-1 Ohkubo, Shinjuku, Tokyo, 169-8555, Japan

    Daisuke Ito & Noboru Murata

Authors
  1. Daisuke Ito
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  2. Noboru Murata
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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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© 2004 Springer-Verlag Berlin Heidelberg

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Cite this paper

Ito, D., Murata, N. (2004). An Approach of Moment-Based Algorithm for Noisy ICA Models. 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_44

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  • DOI: https://doi.org/10.1007/978-3-540-30110-3_44

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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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