Independent component analysis: A neural network approach

Acta Polytechnica Scandinavica Mathematics and Computing Series

Volumn 88, Issue , 1997, Pages

  Hyvärinen, Aapo ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

Blind source separation; Data analysis; Factor analysis; Hebbian learning; Higher order statistics; Independent component analysis; Neural networks; Unsupervised learning

Indexed keywords

EID: 16944363111     PISSN: 14569418     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (45)

1. S. Amari, A. Cichocki, and H.H. Yang. A new learning algorithm for blind source separation. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors. Advances in Neural Information Processing 8 (Proc. NIPS'95), pages 757-763. MIT Press, Cambridge, MA, 1996.
2. S.-I. Amari. Neural learning in structured parameter spaces - natural riemannian gradient. In Advances in Neural Information Processing 9 (Proc. NIPS*96), pages 127-133. MIT Press, Cambridge, MA, 1997.
3. J.J. Atick. Entropy minimization: A design principle for sensory perception? International Journal of Neural Systems, 3:81-90, 1992. Supp. 1992.
4. Pierre F. Baldi and Kurt Hornik. Learning in linear neural networks: A survey. IEEE Transactions on Neural Networks, 6(4):837-858, 1995.
5. Y. Bar-Ness. Bootstrapping adaptive interference cancellers: Some practical limitations. In The Globecom Conf., pages 1251-1255, Miami, 1982. Paper F3.7.
6. H. B. Barlow. Possible principles underlying the transformations of sensory messages. In W. A. Rosenblith, editor. Sensory Communication, pages 217-234. MIT Press, 1961.
7. H. B. Barlow. Single units and sensation: A neuron doctrine for perceptual psychology? Perception, 1:371-394, 1972.
8. H.B. Barlow. Unsupervised learning. Neural Computation, 1:295-311, 1989.
9. H.B. Barlow. What is the computational goal of the neocortex ? In C. Koch and J.L. Davis, editors, Large-scale neuronal theories of the brain. MIT Press, Cambridge, MA, 1994.
10. H.B. Barlow, T.P. Kaushal, and G.J. Mitchison. Finding minimum entropy codes. Neural Computation, 1:412-423, 1989.
11. M. S. Bartlett. A note on the multiplying factors for various chi-square approximations. J. Roy. Stat. Soc., 16 ser B:296-298. 1989.
12. M. Stewart Bartlett and T. J. Sejnowski. Viewpoint invariant face recognition using independent component analysis and attractor networks. In Advances in Neural Information Processing Systems, volume 9, page 817. The MIT Press, 1997.
13. A. Bell and T. J. Sejnowski. Edges are the independent components of natural scenes. In Advances in Neural Information Processing 9 (NIPS*96). pages 831-837. MIT Press. 1997.
14. A.J. Bell and T.J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7:1129-1159, 1995.
15. A. J. Bell and T.J. Sejnowski. Learning higher-order structure of a natural sound. Network, 7:261-266, 1996.
16. A.J. Bell and T.J. Sejnowski. The independent components' of natural scenes are edge filters. Vision Research, 1997. To appear.
17. A. Belouchrani and J.-F. Cardoso. Maximum likelihood source separation by the expectation-maximization technique: deterministic and stochastic implementation. In Proc. NOLTA, pages 49-53, 1995.
18. A. Belouchrani, K. Abed Meraim. J.-F. Cardoso, and E. Moulines. A blind source separation technique based on second order statistics. IEEE Trans, on S.P., 45(2):434-44, 1997.
19. C. M. Bishop. Neural Networks for Pattern Recognition. Clarendon Press, 1995.
20. C. M. Bishop, M. Svensen. and C. K. I. Williams. GTM: The generative topographic mapping. Neural Computation, 1997. To appear.
21. V. Capdevielle, Ch. Serviere, and J.Lacoume. Blind separation of wide-band sources in the frequency domain. In Proc. ICASSP-95, volume 3, pages 2080-2083, Detroit, Michigan, USA, May 9-12 1995.
22. J.-F. Cardoso. Source separation using higher order moments. In Proc. ICASSP'89, pages 2109-2112, 1989.
23. J.-F. Cardoso. Eigen-structure of the fourth-order cumulant tensor with application to the blind source separation problem. In Proc. ICASSP'90, pages 2655-2658, Albuquerque, NM, USA, 1990.
24. J.-F. Cardoso. Super-symmetric decomposition of the fourth-order cumulant tensor, blind identification of more sources than sensors. In Proc. ICASSP'91, pages 3109-3112, 1991.
25. J.-F. Cardoso. Iterative techniques for blind source separation using only fourth-order cumulants. In Proc. EUSIPCO, pages 739-742, Brussels, Belgium, 1992.
26. J.-F. Cardoso. Infomax and maximum likelihood for source separation. IEEE Letters on Signal Processing, 4:112-114, 1997.
27. J.-F. Cardoso and P. Comon. Independent component analysis, a survey of some algebraic methods. In Proc. ISCAS'96, volume 2, pages 93-96, 1996.
28. J.-F. Cardoso and B. Hvam Laheld. Equivariant adaptive source separation. IEEE Trans. on Signal Processing, 44(12):3017-3030, 1996.
29. A. Cichocki, S. I. Amari, and R. Thawonmas. Blind signal extraction using self-adaptive non-linear hebbian learning rule. In Proc. NOLTA ''96, pages 377-380, 1996.
30. A. Cichocki, R.E. Bogner, L. Moszczynski, and K. Pope. Modified Herault-Jutten algorithms for blind separation of sources. Digital Signal Processing, 7:80-93, 1997.
31. A. Cichocki and W. Kasprzak. Nonlinear learning algorithms for blind separation of natural images. Neural Network World, 4:515-523, 1996.
32. A. Cichocki and R. Unbehauen. Neural Networks for Signal Processing and Optimization. Wiley, 1994.
33. A. Cichocki and R. Unbehauen. Robust neural networks with on-line learning for blind identification and blind separation of sources. IEEE Trans, on Circuits and Systems, 43(11):894-906, 1996.
34. A. Cichocki, R. Unbehauen, L. Moszczynski, and E. Rummert. A new on-line adaptive algorithm for blind separation of source signals. In Proc. Int. Symposium on Artificial Neural Networks ISANN-94, pages 406-411, Tainan, Taiwan, 1994.
35. P. Comon. Blind identification in presence of noise. In Signal Processing VI: Theories and Application (Proc. EUSIPCO), pages 835-838. Elsevier, 1992.
36. P. Comon. Independent component analysis - a new concept? Signal Processing, 36:287-314, 1994.
37. D. Cook, A. Buja. and J. Cabrera. Projection pursuit indexes based on orthonormal function expansions. J. of Computational and Graphical Statistics, 2(3):225-250, 1993.
38. J. G. Daugman. Entropy reduction and decorrelation in visual coding by oriented neural receptive fields. IEEE Trans, on Biomedical Engineering, 36:107-114, 1989.
39. G. Deco and D. Obradovic. Linear redundancy reduction learning. Neural Networks, 8(5):751-755, 1995.
40. N. Delfosse and P. Loubaton. Adaptive blind separation of independent sources: a deflation approach. Signal Processing, 45:59-83, 1995.
41. A. P. Dempster, N. Laird, and D. Rubin. Maximum likelihood for incomplete data via the EM algorithm. J. of the Royal Statistical Society, ser. B, 39:1-38, 1977.
42. K. I. Diamantaras and S. Y. Kung. Principal Component Neural Networks: Theory and Applications. Adaptive and Learning Systems for Signal Processing, Communications, and Control. John Wiley & Sons, Inc., 1996.
43. D. Donoho. On minimum entropy deconvolution. In Applied Time Series Analysis II, pages 565-608. Academic Press, 1981.
44. D.J. Field. What is the goal of sensory coding? Neural Computation, 6:559-601, 1994.
45. J. H. Friedman and J. W. Tukey. A projection pursuit algorithm for exploratory data analysis. IEEE Trans, of Computers. c-23(9):881-890. 1974.