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Newton-Like Methods for Nonparametric Independent Component Analysis

  • Hao Shen
  • Knut Hüper
  • Alexander J. Smola
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)

Abstract

The performance of ICA algorithms significantly depends on the choice of the contrast function and the optimisation algorithm used in obtaining the demixing matrix. In this paper we focus on the standard linear nonparametric ICA problem from an optimisation point of view. It is well known that after a pre-whitening process, the problem can be solved via an optimisation approach on a suitable manifold. We propose an approximate Newton’s method on the unit sphere to solve the one-unit linear nonparametric ICA problem. The local convergence properties are discussed. The performance of the proposed algorithms is investigated by numerical experiments.

Keywords

Independent Component Analysis Independent Component Analysis Blind Source Separation Contrast Function Independent Component Analysis Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hao Shen
    • 1
    • 3
  • Knut Hüper
    • 1
    • 3
  • Alexander J. Smola
    • 2
    • 4
  1. 1.Systems Engineering and Complex Systems Research Program 
  2. 2.Statistical Machine Learning Research ProgramNational ICT AustraliaCanberraAustralia
  3. 3.Department of Information Engineering 
  4. 4.Computer Science Laboratory, Research School of Information Sciences and EngineeringThe Australian National UniversityCanberraAustralia

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