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An Extended Online Fast-ICA Algorithm

  • Gang Wang
  • Ni-ni Rao
  • Zhi-lin Zhang
  • Quanyi Mo
  • Pu Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

Hyävrinen and Oja have proposed an offline Fast-ICA algorithm. But it converge slowly in online form. By using the online whitening algorithm, and applying nature Riemannian gradient in Stiefel manifold, we present in this paper an extended online Fast-ICA algorithm, which can perform online blind source separation (BSS) directly using unwhitened observations. Computer simulation resluts are given to demonstrate the effectiveness and validity of our algorithm.

Keywords

Independent Component Analysis Online Algorithm Independent Component Analysis Blind Source Separation Nature Gradient 
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

  • Gang Wang
    • 1
    • 2
  • Ni-ni Rao
    • 1
  • Zhi-lin Zhang
    • 2
  • Quanyi Mo
    • 2
  • Pu Wang
    • 2
  1. 1.School of life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.Blind Source Separation GroupUniversity of Electronic Science and Technology of ChinaChengduChina

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