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Linear Dimension Reduction Based on the Fourth-Order Cumulant Tensor

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Book cover Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3697))

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Abstract

In high dimensional data analysis, finding non-Gaussian components is an important preprocessing step for efficient information processing. By modifying the contrast function of JADE algorithm for Independent Component Analysis, we propose a new linear dimension reduction method to identify the non-Gaussian subspace based on the fourth-order cumulant tensor. A numerical study demonstrates the validity of our method and its usefulness for extracting sub-Gaussian structures.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .

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References

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Author information

Authors and Affiliations

  1. Fraunhofer FIRST.IDA, Kekuléstr. 7, 12439, Berlin, Germany

    M. Kawanabe

Authors
  1. M. Kawanabe
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Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Kawanabe, M. (2005). Linear Dimension Reduction Based on the Fourth-Order Cumulant Tensor. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_25

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  • DOI: https://doi.org/10.1007/11550907_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28755-1

  • Online ISBN: 978-3-540-28756-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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