Temporal Decorrelation as Preprocessing for Linear and Post-nonlinear ICA

  • Juha Karvanen
  • Toshihisa Tanaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3195)

Abstract

We present a straightforward way to use temporal decorrelation as preprocessing in linear and post-nonlinear independent component analysis (ICA) with higher order statistics (HOS). Contrary to the separation methods using second order statistics (SOS), the proposed method can be applied when the sources have similar temporal structure. The main idea is that componentwise decorrelation increases non-Gaussianity and therefore makes it easier to separate sources with HOS ICA. Conceptually, the non-Gaussianizing filtering matches very well with the Gaussianization used to cancel the post-nonlinear distortions. Examples demonstrating the consistent improvement in the separation quality are provided for the both linear and post-linear cases.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Juha Karvanen
    • 1
  • Toshihisa Tanaka
    • 1
  1. 1.Laboratory for Advanced Brain Signal ProcessingBrain Science Institute, RIKENSaitamaJapan

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