Information Maximization and Independent Component Analysis: Is There a Difference?

Neural Computation

Volumn 10, Issue 8, 1998, Pages 2085-2101

  Obradovic, D ^a   Deco, G ^a  

^a SIEMENS AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; HUMAN; PATTERN RECOGNITION; PROBABILITY; VISION;

ALGORITHMS; HUMANS; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PATTERN RECOGNITION, VISUAL; PROBABILITY; VISUAL PERCEPTION;

EID: 0032533057     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976698300016972     Document Type: Article

References (25)

1. Amari, S., Cichocki, A., & Yang, H. H. (1996). A new learning algorithm for blind source separation. In G. Tesauro, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in neural information processing, 8 (pp. 757-763). Cambridge, MA: MIT Press.
2. Atick, J., & Redlich, A. (1990). Towards a theory of early visual processing. Neural Computation, 2, 308-320.
3. Atick, J. & Redlich, A. (1992). What does the retina know about natural scenes? Neural Computation, 4, 196-210.
4. Attneave, F. (1954). Informational aspects of visual perception. Psychological Review, 61, 183-193.
5. Barlow, H. (1989). Unsupervised learning. Neural Computation, 1, 295-311.
6. Barlow, H., Kaushal, T., & Mitchison, G. (1989). Finding minimum entropy codes. Neural Computation, 1, 412-423.
7. Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7, 1129-1160.
8. Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36, 287-314.
9. Deco, G., & Brauer, W. (1994a). Higher order statistics with neural networks. In G. Tesauro, D. Touretzky, & T. Leen (Eds.), Advances in neural information processing, 7 (pp. 247-254). Cambridge, MA: MIT Press.
10. Deco, G., & Brauer, W. (1995a). Nonlinear higher order statistical decorrelation by volume-conserving neural networks. Neural Networks, 8, 525-535.
11. Deco, G., & Obradovic, D. (1994b). Principal component analysis: A factorial learning approach. In International Conference on Artificial Neural Networks (Vol. 2, pp. 1059-1062). Berlin: Springer-Verlag.
12. Deco, G., & Obradovic, D. (1995b). Rotation based redundancy reduction learning. Neural Networks, 8, 751-755.
13. Deco, G., & Obradovic, D. (1996). An information-theoretic approach to neural computing. New York: Springer-Verlag.
14. Lee, T., Girolami, M., Bell, A. J., & Sejnowski, T. J. (1998) A unifying information-theoretic framework for independent component analysis. International Journal on Mathematical and Computer Modeling. In press.
15. Koecher, M. (1983). Lineare Algebra und analytische Geometrie. New York: Springer-Verlag.
16. Nadal, J-P., & Parga, N. (1994). Non-linear neurons in the low noise limit: A factorial code maximizes information transfer. Network, 5, 565-581.
17. Nadal, J.-P., & Parga, N. (1997). Redundancy reduction and independent component analysis: Conditions on cumulants and adaptive approaches. Neural Computation, 9, 1421-1456.
18. Obradovic, D., & Deco, G. (1994). Linear feature extraction in networks with lateral connections. In IEEE World Congress on Computational Intelligence (Vol. 2, pp. 686-691).
19. Obradovic, D., & Deco, G. (1995). Linear feature extraction in non-gaussian networks. In World Congress on Neural Networks (Vol. 2, pp. 523-527).
20. Obradovic, D., & Deco, G. (1996). An information theory based learning paradigm for linear feature extraction. Neurocomputing, 12, 203-221.
21. Parra, L., Deco, G., & Miesbach, S. (1995). Redundancy reduction with information preserving nonlinear maps. Networks: Computation in Neural Systems, 6, 61-72.
22. Pearlmutter, B. A., & Parra, L. C. (1996). A context-sensitive generalization of ICA. In Proc. ICONIP'96, Japan.
23. Redlich, A. N. (1993a). Redundancy reduction as a strategy for unsupervised learning. Neural Computation, 5, 289-304.
24. Redlich, A. N. (1993b). Supervised factorial learning. Neural Computation, 5, 750-766.
25. Zipf, G. (1949). Human behavior and the principle of least effort. Reading, MA: Addison-Wesley.