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Independent Component Analysis Via Copula Techniques

  • Ray-Bing Chen
  • Meihui Guo
  • Wolfgang Härdle
  • Shih-Feng Huang

Independent component analysis (ICA) is a modern factor analysis tool de- veloped in the last two decades. Given p-dimensional data, we search for that linear combination of data which creates (almost) independent components. Here copulae are used to model the p-dimensional data and then independent components are found by optimizing the copula parameters. Based on this idea, we propose the COPICA method for searching independent components. We illustrate this method using several blind source separation examples, which are mathematically equivalent to ICA problems. Finally performances of our method and FastICA are compared to explore the advantages of this method.

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Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2008-004.

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Length: 24 pages
Date of creation: Jan 2008
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2008-004
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