Adaptive nonlinear PCA agorithms for blind source separation without prewhitening

IEEE Transactions on Circuits and Systems I: Regular Papers

Volumn 53, Issue 3, 2006, Pages 745-753

  Zhu, Xiao Long ^a,c   Zhang, Xian Da ^a   Ding, Zi Zhe ^a   Jia, Ying ^b  

^a TSINGHUA UNIVERSITY   (China)

^b INTEL CHINA RESEARCH CENTER   (China)

^c Alcatel Shanghai Bell Co Ltd   (China)

Author keywords

Blind source separation (BSS); Independent component analysis; Natural gradient; Nonlinear principal component analysis (NPCA); Recursive least squares; Whitening

Indexed keywords

ADAPTIVE ALGORITHMS; COMPUTER SIMULATION; INDEPENDENT COMPONENT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS;

LEAST-MEAN-SQUARE ALGORITHM; NATURAL GRADIENT; NONLINEAR PRINCIPAL COMPONENT ANALYSIS; PREWHITENING PROCESS;

BLIND SOURCE SEPARATION;

EID: 33645822774     PISSN: 10577122     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCSI.2005.858489     Document Type: Article

References (28)

1. M. Girolami, Self-Organizing Neural Networks: Independent Component Analysis and Blind Source Separation. London, U.K.: Springer-Verlag, 1999.
2. A. Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis. New York: Wiley, 2001.
3. L. Tong, R. W. Liu, V. C. Soon, and Y. F. Huang, "Indeterminacy and identifiability of blind identification," IEEE Trans. Circuits Syst., vol. 38, no. 5, pp. 499-509, May 1991.
4. P. Comon, "Independent component analysis, a new concept?," Signal Process., vol. 36, no. 3, pp. 287-314, Mar. 1994.
5. A. Cichocki, R. Unbehauen, and E. Rummert, "Robust learning algorithm for blind separation of signals," Electron. Lett., vol. 30, no. 17, pp. 1386-1387, Aug. 1994.
6. A. Cichocki and R. Unbehauen, "Robust neural networks with online learning for blind identification and blind separation of sources," IEEE Trans. Circuits Syst. I, Fundam. Theory Appl., vol. 43, no. 11, pp. 894-906, Nov. 1996.
7. S. Amari, A. Cichocki, and H. H. Yang, "A new algorithm for blind signal separation," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 1996, vol. 8, pp. 757-763.
8. S. Amari, "Natural gradient works efficiently in learning," Neural Comput., vol. 10, no. 2, pp. 251-276, Feb. 1998.
9. J. F. Cardoso and H. Laheld, "Equivariant adaptive source separation," IEEE Trans. Signal Process., vol. 44, no. 12, pp. 3017-3029, Dec. 1996.
10. E. Oja, "The nonlinear PCA learning in independent component analysis," Neurocomputing, vol. 17, no. 1, pp. 25-46, Sep. 1997.
11. J. Karhunen, P. Pajunen, and E. Oja, "The nonlinear PCA criterion in blind source separation: Relations with other approaches," Neurocomputing, vol. 22, no. 1-3, pp. 5-20, Nov. 1998.
12. P. Pajunen and J. Karhunen, "Least-squares methods for blind source separation based on nonlinear PCA," Int. J. Neural Syst., vol. 8, no. 5-6, pp. 601-612, Oct./Dec. 1998.
13. X. L. Zhu and X. D. Zhang, "Adaptive RLS algorithm for blind source separation using a natural gradient," IEEE Signal Process. Lett., vol. 9, no. 12, pp. 432-435, Dec. 2002.
14. J. F. Cardoso and A. Souloumiac, "Blind beamforming for non-Gaussian signals," Inst. Elect. Eng. Proc. F, Radar and Signal Processing, vol. 140, no. 6, pp. 270-362, Dec. 1993.
15. A. Hyvarinen, "Fast and robust fixed-point algorithms for independent component analysis," IEEE Trans. Neural Netw., vol. 10, no. 3, pp. 626-634, May 1999.
16. X. L. Zhu, X. D. Zhang, and J. M. Ye, "Natural gradient-based recursive least-squares algorithm for blind source separation," Sci. China Ser. F, vol. 47, no. 1, pp. 55-65, Jan. 2004.
17. E. Moreau and N. T. Moreau, "Nonsymmetrical contrasts for source separation," IEEE Trans. Signal Process., vol. 47, no. 8, pp. 2241-2252, Aug. 1999.
18. L. Xu, "Least mean square error reconstruction principle for self-organizing neural-nets," Neural Netw., vol. 6, no. 5, pp. 627-648, 1993.
19. P. Smaragdis, "Blind separation of convolved mixtures in the frequency domain," Neurocomputing, vol. 22, no. 1-3, pp. 21-34, Nov. 1998.
20. H. Sawada, R. Mukai, S. Araki, and S. Makino, "A robust and precise method for solving the permutation problem of frequency-domain blind source separation," IEEE Trans. Speech Audio Process., vol. 12, no. 5, pp. 530-538, Sep. 2004.
21. B. Yang, "Projection approximation subspace tracking," IEEE Trans. Signal Process., vol. 43, no. 1, pp. 95-107, Jan. 1995.
22. A. Edelman, T. Arias, and S. T. Smith, "The geometry of algorithms with orthogonality constraints," SIAM J. Matrix Anal. Appl., vol. 20, no. 2, pp. 303-353, 1998.
23. S. Amari, "Natural gradient learning for over- and under-complete bases in ICA," Neural Comput., vol. 11, no. 8, pp. 1875-1883, Nov. 1999.
24. S. Fiori, "A theory for learning by weight flow on Stiefel-Grassman manifold," Neural Comput., vol. 13, no. 7, pp. 1625-1647, Jul. 2001.
25. S. Haykin, Adaptive Filter Theory, 4th ed. Englewood Cliffs, NJ: Prentice-Hall, 2002.
26. E. Eleftheriou and D. Falconer, "Tracking properties and steady-state performance of RLS adaptive filter algorithm," IEEE Trans. Acoust. Speech Signal Process., vol. 34, no. 5, pp. 1097-1110, Oct. 1986.
27. E. Oja and M. Plumbley, "Blind separation of positive sources by globally convergent gradient search," Neural Comput., vol. 16, no. 9, pp. 1811-1825, Sept. 2004.
28. J. M. Ye, X. L. Zhu, and X. D. Zhang, "Adaptive blind separation with an unknown number of sources," Neural Comput., vol. 16, no. 8, pp. 1641-1660, Aug. 2004.