Two gradient descent algorithms for blind signal separation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1112 LNCS, Issue , 1996, Pages 287-292

  Yang, H H ^a   Amari, S ^a  

^a RIKEN   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BLIND SOURCE SEPARATION; NEURAL NETWORKS;

APPROXIMATION METHODS; BLIND SEPARATION ALGORITHMS; BLIND SIGNAL SEPARATION; DE-MIXING; GRADIENT DESCENT ALGORITHMS; MUTUAL INFORMATIONS; NATURAL GRADIENT; PERFORMANCE FUNCTIONS;

ALGORITHMS;

EID: 0347682584     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-61510-5_51     Document Type: Conference Paper

References (8)

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2. S. Amari, A. Cichocki, and H. H. Tang. Recurrent neural networks for blind separation of sources. In Proceedings 1995 International Symposium on Nonlinear Theory and Applications, volume I, pages 37-42, December 1995.
3. S. Amari, A. Cichocki, and H. H. Yang. A new learning algorithm for blind signal separation. In Advances in Neural Information Processing Systems, 8, eds. David S. Touretzky, Michael C. Mozer and Michael E. Hasselmo, MIT Press: Cambridge, MA. (to appear), 1996.
4. A. J. Bell and T. J. Sejnowski. An information-maximisation approach to blind separation and blind deconvolution. Neural Computation, 7:1129-1159, 1995.
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