Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks

Volumn 10, Issue 3, 1999, Pages 626-634

  Hyvärinen, Aapo ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; ESTIMATION; FUNCTIONS; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; OPTIMIZATION; RANDOM PROCESSES; STATISTICAL METHODS; VECTORS;

CONTRAST FUNCTION; DIFFERENTIAL ENTROPY; INDEPENDENT COMPONENT ANALYSIS; MAXIMUM ENTROPY APPROXIMATION; MULTIDIMENSIONAL RANDOM VECTOR;

NEURAL NETWORKS;

EID: 0032629347     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.761722     Document Type: Article

References (38)

1. S. Amari, A. Cichocki, and H. H. Yang, "A new learning algorithm for blind source separation," in Advances in Neural Inform. Processing 8. Cambridge: MIT Press, 1996, pp. 757-763.
2. H. B. Barlow, "Possible principles underlying the transformations of sensory messages," in Sensory Communication, W. A. Rosenblith, Ed. Cambridge, MA: MIT Press, 1961, pp. 217-234.
3. A. J. Bell and T. J. Sejnowski, "An information-maximization approach to blind separation and blind deconvolution," Neural Comput., vol. 7, pp. 1129-1159, 1995.
4. _, "The 'independent components' of natural scenes are edge filters," Vision Res., vol. 37, pp. 3327-3338, 1997.
5. J.-F. Cardoso and B. Hvam Laheld, "Equivariant adaptive source separation," IEEE Trans. Signal Processing, vol. 44, pp. 3017-3030, 1996.
6. A. Cichocki and R. Unbehauen, Neural Networks for Signal Processing and Optimization. New York: Wiley, 1994.
7. P. Comon, "Independent component analysis - A new concept?," Signal Processing, vol. 36, pp. 287-314, 1994.
8. T. M. Cover and J. A. Thomas, Elements of Information Theory. New York: Wiley, 1991.
9. N. Delfosse and P. Loubaton, "Adaptive blind separation of independent sources: A deflation approach," Signal Processing, vol. 45, pp. 59-83, 1995.
10. The Fast-ICA MATLAB package. Available WWW: http://www. cis.hut.fi/projects/ica/fastica/1998
11. J. H. Friedman and J. W. Tukey, "A projection pursuit algorithm for exploratory data analysis," IEEE Trans. Comput., vol. c-23, pp. 881-890, 1974.
12. J. H. Friedman, "Exploratory projection pursuit," J. Amer. Statist. Assoc., vol. 82, no. 397, pp. 249-266, 1987.
13. X. Giannakopoulos, J. Karhunen, and E. Oja, "Experimental comparison of neural ICA algorithms," in Proc. Int. Conf Artificial Neural Networks (ICANN'98), Skövde, Sweden, 1998, pp. 651-656.
14. F. R. Hampel, E. M. Ronchetti, P. J. Rousseuw, and W. A. Stahel, Robust Statistics. New York: Wiley, 1986.
15. H. H. Harman, Modern Factor Analysis, 2nd ed. Chicago, IL: Univ. Chicago Press, 1967.
16. P. J. Huber, "Projection pursuit," Ann. Statist., vol. 13, no. 2, pp. 435-475, 1985.
17. A. Hyvärinen, "A family of fixed-point algorithms for independent component analysis," in Proc. IEEE Int. Conf. Acoust., Speech Signal Processing (ICASSP'97), Munich, Germany, 1997, pp. 3917-3920.
18. _, "One-unit contrast functions for independent component analysis: A statistical analysis," in Neural Networks Signal Processing VII, Proc. IEEE Workshop Neural Networks for Signal Processing, Amelia Island, FL, 1997, pp. 388-397.
19. _, "New approximations of differential entropy for independent component analysis and projection pursuit," in Advances Neural Inform. Processing Syst. 10. MIT Press, 1998, pp. 273-279.
20. _, "Fast independent component analysis with noisy data using gaussian moments," in Proc. Int. Symp. Circuits Syst., Orlando, FL, 1999, to be published.
21. _, "The fixed-point algorithm and maximum likelihood estimation for independent component analysis," Neural Processing Lett., 1999, to be published.
22. A. Hyvärinen, R. Cristescu, and E. Oja, "A fast algorithm for estimating overcomplete ICA bases for image windows," in Proc. Int. Joint Conf. Neural Networks, Washington, D.C., 1999, submitted.
23. A. Hyvärinen and E. Oja, "A fast fixed-point algorithm for independent component analysis," Neural Comput., vol. 9, no. 7, pp. 1483-1492, 1997.
24. _, "Independent component analysis by general nonlinear Hebbian-like learning rules," Signal Processing, vol. 64, no. 3, pp. 301-313, 1998.
25. A. Hyvärinen, E. Oja, P. Hoyer, and J. Hurri, "Image feature extraction by sparse coding and independent component analysis," in Proc. Int. Conf. Pattern Recognition (ICPR'98), Brisbane, Australia, 1998, pp. 1268-1273.
26. M. C. Jones and R. Sibson, "What is projection pursuit?," J. Roy. Statist. Soc., Ser. A, vol. 150, pp. 1-36, 1987.
27. C. Jutten and J. Herault, "Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic architecture," Signal Processing, vol. 24, pp. 1-10, 1991.
28. J. Karhunen, E. Oja, L. Wang, R. Vigario, and J. Joutsensalo, "A class of neural networks for independent component analysis," IEEE Trans. Neural Networks, vol. 8, pp. 486-504, 1997.
29. D. G. Luenberger, Optimization by Vector Space Methods. New York: Wiley, 1969.
30. E. Oja, "Principal components, minor components, and linear neural networks," Neural Networks, vol. 5, pp. 927-935, 1992.
31. _, "The nonlinear PCA learning rule in independent component analysis," Neurocomputing, vol. 17, no. 1, pp. 25-46, 1997.
32. B. A. Olshausen and D. J. Field, "Emergence of simple-cell receptive field properties by learning a sparse code for natural images," Nature, vol. 381, pp. 607-609, 1996.
33. A. Papoulis, Probability, Random Variables, and Stochastic Processes, 3rd ed. New York: McGraw-Hill, 1991.
34. D.-T. Pham, P. Garrat, and C. Jutten, "Separation of a mixture of independent sources through a maximum likelihood approach," in Proc. EUSIPCO, pp. 771-774, 1992.
35. J. H. van Hateren and A. van der Schaaf, "Independent component filters of natural images compared with simple cells in primary visual cortex," Proc. Roy. Soc. Ser. B, vol. 265, pp. 359-366, 1998.
36. R. Vigário, "Extraction of ocular artifacts from EEG using independent component analysis," Electroenceph. Clin. Neurophysiol., vol. 103, no. 3, pp. 395-404, 1997.
37. R. Vigário, V. Jousmäki, M. Hämäläinen, R. Hari, and E. Oja, "Independent component analysis for identification of artifacts in magnetoencephalographic recordings," in Advances Neural Inform. Processing 10 (Proc. NIPS'97). Cambridge, MA: MIT Press, 1998, pp. 229-235.
38. R. Vigário, J. Särelä, and E. Oja, "Independent component analysis in wave decomposition of auditory evoked fields," in Proc. Int. Conf. Artificial Neural Networks (ICANN'98), Skövde, Sweden, 1998, pp. 287-292.