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Information-Theoretic Model Selection for Independent Components

  • Claudia Plant
  • Fabian J. Theis
  • Anke Meyer-Baese
  • Christian Böhm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6365)

Abstract

Independent Component Analysis (ICA) is an essential building block for data analysis in many applications. Selecting the truly meaningful components from the result of an ICA algorithm, or comparing the results of different algorithms, however, are non-trivial problems. We introduce a very general technique for evaluating ICA results rooted in information-theoretic model selection. The basic idea is to exploit the natural link between non-Gaussianity and data compression: The better the data transformation represented by one or several ICs improves the effectiveness of data compression, the higher is the relevance of the ICs. In an extensive experimental evaluation we demonstrate that our novel information-theoretic measure robustly selects the most interesting components from data without requiring any assumptions or thresholds.

Keywords

Probability Density Function Independent Component Analysis Gaussian Mixture Model Data Compression Reconstruction Error 
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 2010

Authors and Affiliations

  • Claudia Plant
    • 1
  • Fabian J. Theis
    • 2
  • Anke Meyer-Baese
    • 1
  • Christian Böhm
    • 3
  1. 1.Florida State University 
  2. 2.Helmholtz Zentrum München 
  3. 3.University of Munich 

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