Independent component analysis by the information-theoretic approach with mixture of densities

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn 3, Issue , 1997, Pages 1821-1826

  Xu, Lei ^a   Cheung, Chi Chiu ^a   Yang, Howard Hua ^b   Amari, Shun Ichi ^b  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b RIKEN   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS OF DISTRIBUTIONS; FLEXIBLE MODEL; IMPLEMENTATION TECHNIQUES; INDEPENDENT COMPONENTS; INFORMATION-THEORETIC APPROACH; MARGINAL DENSITIES; RECOVERED SIGNALS; SOURCE SIGNALS;

ALGORITHMS; INDEPENDENT COMPONENT ANALYSIS; MIXTURES; NEURAL NETWORKS; APPROXIMATION THEORY; INFORMATION THEORY; MATHEMATICAL MODELS; PROBABILITY DENSITY FUNCTION;

INFORMATION THEORY; NEURAL NETWORKS;

INDEPENDENT COMPONENT ANALYSIS;

EID: 0030706830     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNN.1997.614175     Document Type: Conference Paper

References (9)

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2. A.J. Bell and T.J. Sejnowski, An information-maximatization approach to blind separation and blind deconvolution, Neural Computation 7 (1995) 1129-1159.
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6. L. Xu, A unified learning scheme: Bayesian-Kullback YING-YANG Machine, in David S. Touret-zky, Michael C. Mozer fc d Michael E. Hasselmo, eds, Advances in Neural Information Processing 8 (MIT Press: Cambridge, MA. 1996) 444-450.
7. L. Xu, Bayesian-Kullback YING-YANG Machine: reviews and new results, in Progress in Neural Information Processing: Proc. Intl. Conf. on Neural Information Processing (ICONIP 96) (Hong Kong, Sept 24-27, 1996; Springer-Verlag: Singapore 1996) 59-67.
8. L. Xu and S.-I. Amari, A general independent component analysis framework based on Bayesian-Kullback Ying-Yang Learning, in Progress in Neural Information Processing: Proc. Intl. Conf. on Neural Information Processing (ICONIP 96) (Hong Kong, Sept 24-27, 1996; Springer-Verlag: Singapore 1996) 1235-1239.
9. L. Xu, C.C. Cheung, J. Ruan and S.-I. Amari, Nonlinearity and separation capability: Further justification for the ICA algorithm with mixture of densities, accepted by the European Symposium on Artificial Neural Networks '97 (ESANN'97).