A Unifying Information-Theoretic Framework for ICA

  • Te-Won Lee


This chapter shows that different theories recently proposed for ICA lead to the same iterative learning algorithm for blind separation of mixed independent sources. Those theories are reviewed and it is suggested that information theory can be used to unify several lines of research.


Mutual Information Blind Source Separation Contrast Function Blind Deconvolution Joint Entropy 
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 Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Te-Won Lee
    • 1
  1. 1.Computational Neurobiology LaboratoryThe Salk InstituteLa JollaUSA

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