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Contrast Functions for Independent Subspace Analysis

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7191)

Abstract

We consider the Independent Subspace Analysis problem from the point of view of contrast functions, showing that contrast functions are able to partially solve the ISA problem. That is, basic ICA can solve the ISA problem up to within-subspace separation/analysis. We define sub- and super-Gaussian subspaces and extend to ISA a previous result on freedom of ICA from local optima. We also consider new types of dependent densities that satisfy or violate the entropy power inequality (EPI) condition.

Keywords

  • Mutual Information
  • Fisher Information
  • Independent Component Analysis
  • Projection Pursuit
  • Contrast Function

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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Palmer, J.A., Makeig, S. (2012). Contrast Functions for Independent Subspace Analysis. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_15

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  • DOI: https://doi.org/10.1007/978-3-642-28551-6_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28550-9

  • Online ISBN: 978-3-642-28551-6

  • eBook Packages: Computer ScienceComputer Science (R0)