Adaptive Online Learning Algorithms for Blind Separation: Maximum Entropy and Minimum Mutual Information

Neural Computation

Volumn 9, Issue 7, 1997, Pages 1457-1482

  Yang, Howard Hua ^a   Amari, Shun Ichi ^a  

^a RIKEN   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0000056917     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/neco.1997.9.7.1457     Document Type: Article

References (13)

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4. Amari, S., Cichocki, A., & Yang, H. H. (1996). A new learning algorithm for blind signal separation. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo Advances in neural information processing systems, 8, (pp. 757-763). Cambridge, MA: MIT Press.
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7. Bell, A. J., & Sejnowski, T. J. (1995b). Fast blind separation based on information theory. In Proceedings 1995 International Symposium on Nonlinear Theory and Applications (vol. 1, pp. 43-47). Las Vegas.
8. Cardoso, J.-F., & Laheld, B. 1996. Equivariant adaptive source separation. IEEE Trans. on Signal Processing, 44(12), 3017-3030.
9. Cichocki, A., Unbehauen, R., Moszczyński, L., & Rummert, E. (1994). A new online adaptive learning algorithm for blind separation of source signals. In ISANN94 (pp. 406-411). Taiwan.
10. Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36, 287-314.
11. Deco, G., & Brauer, W. (1996). Nonlinear higher-order statistical decorrelation by volume-conserving neural architectures. Neural Networks, 8(4), 525-535.
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13. Stuart, A., & Ord, J. K. (1994). Kendall's advanced theory of statistics. London: Edward Arnold.