Blind source separation and tracking using nonlinear PCA criterion: A least-squares approach

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn 4, Issue , 1997, Pages 2147-2152

  Karhunen, Juha ^a   Pajunen, Petteri ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONALLY EFFICIENT; LEAST-SQUARES APPROACH; LINEAR MIXTURES; NEURAL LEARNING; NONSTATIONARY; PRIORI INFORMATION; SOURCE SIGNALS; STOCHASTIC GRADIENT;

ALGORITHMS; MIXTURES; NEURAL NETWORKS; SIGNAL PROCESSING; FEATURE EXTRACTION; LEARNING ALGORITHMS; LEAST SQUARES APPROXIMATIONS; STOCHASTIC CONTROL SYSTEMS;

BLIND SOURCE SEPARATION; SPEECH RECOGNITION;

BLIND SOURCE SEPARATION; BLIND SOURCE TRACKING;

EID: 0030677955     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNN.1997.614238     Document Type: Conference Paper

References (17)

1. J. Karhunen, A. Hyvarinen, R. Vigario, J. Hurri, and E. Oja, "Applications of neural blind separation to signal and image processing," to appear in Proc. 1997 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP'97), Munich, Germany, April 1997.
2. P. Comon, "Independent component analysis- A new concept?," Signal Processing, vol. 36, pp. 287314, 1994.
3. J. Karhunen, "Neural approaches to independent component analysis and source separation," in Proc. J,th European Syrup, on Artificial Neural Networks (ESANN'96), Bruges, Belgium, April 1996, pp. 249-266 (Invited paper).
4. S. Amari, A. Cichocki, and H. Yang, "A new learning algorithm for blind signal separation," in Advances in Neural Information Processing Systems 8, D. S. Touretzky et al. (Eds. ). Cambridge, MA: MIT Press, 1996, pp. 757-763.
5. A. Bell and T. Sejnowski, "An information-maximisation approach to blind separation and blind deconvolution," Neural Computation, vol. 7, pp. 1129-1159, 1995.
6. 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-1, vol. 43, pp. 894-906, November 1996.
7. C. Jutten and J. Herault, "Blind separation of sources, part I: an adaptive algorithm based on neuromimetic architecture," Signal Processing, vol. 24, no. 1, pp. 1-10, July 1991.
8. J. Karhunen, L. Wang, and R. Vigario, "Nonlinear PCA type approaches for source separation and independent component analysis," in Proc. 1995 IEEE Int. Conf. on Neural Networks, Perth, Australia, November 1995, pp. 995-1000.
9. L. Wang, J. Karhunen, and E. Oja, "A bigradient optimization approach for robust PCA, MCA, and source separation," in Proc. 1995 IEEE Int. Conf. on Neural Networks, Perth, Australia, November 1995, pp. 1684-1689.
10. B. Yang, "Projection approximation subspace tracking," IEEE Trans, on Signal Processing, vol. 43, pp. 95-107, January 1995.
11. E. Moreau and O. Macchi, "High-order contrasts for self-adaptive source separation," Int. J. of Adaptive Control and Signal Processing, vol. 10, pp. 19-46, 1996.
12. J. Karhunen and J. Joutsensalo, "Representation and separation of signals using nonlinear PCA type learning," Neural Networks, vol. 7, no. 1, pp. 113-127, 1994.
13. M. Girolami and C. Fyfe, "Stochastic ICA contrast maximisation using Oja's nonlinear PCA algorithm," submitted to Int. J. Neural Systems, July 1996.
14. E. Oja, "The Nonlinear PCA learning rule and signal separation-mathematical analysis," Helsinki Univ. of Technology, Lab. of Computer and Information Science, Finland, Report A26, August 1995. Submitted to a journal
15. J. Karhunen and J. Joutsensalo, "Generalizations of principal component analysis, optimization problems, and neural networks," Neural Networks, vol. 8, no. 4, pp. 549-562, 1995.
16. K. Diamantaras and S. Kung, Principal Component Networks-Theory and Applications. New York: John Wiley, 1996.
17. J. Mendel, Lessons in Estimation Theory for Signal Processing, Communications, and Control. Prentice-Hall, 1995.