Unified parametric and nonparametric ICA algorithms for hybrid source signals and stability analysis

International Journal of Innovative Computing, Information and Control

Volumn 4, Issue 4, 2008, Pages 933-942

  Wang, Fasong ^a   Li, Hongwei ^a   Li, Rui ^b  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

^b HENAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Blind source separation (BSS); GGD; Independent component analysis (ICA); K nearest neighbor nonparametric estimation; Natural gradient; Score function

Indexed keywords

EID: 48249088442     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (19)

1. A. Cichocki and S. Amari, Adaptive Blind Signal and Adaptive Blind Signal and Image Processing, John Wiley & Sons, New York, 2002.
2. A. Hyvarinen, J. Kaxhunen and E. Oja, Independent Component Analysis, John Wiley & Sons, New York, 2001.
3. P. Comon, Independent component analysis, a new concept?, Signal Processing, vol.36, no.3, pp.287-314, 1994.
4. A. Bell and T. Seinowski, An information maximization approach to blind separation and blind deconvolution, Neural Computation, vol.7, no.6, pp.1129-1159, 1995.
5. T. W. Lee, M. Girolami and T. Sejnowski, Independent component analysis using an extend infomax algorithm for mixed sub-Gaussian and super-Gaussian sources, Neural Computation, vol.11, no.2, pp.417-441, 1999.
6. A. Hyvarinen and E. Oja, A fast fixed-point algorithm for independent component analysis, Neural Computation, vol.9, no.7, pp.1483-1492, 1998.
7. F. S. Wang, H. W. Li, R. Li and Y. T. Shen, Novel algorithm for independent component analysis with flexible score functions, Proc. of the 7th Int. Conf. Signal Processing Proceedings, IEEE Press, vol.1, pp.132-135, 2004.
8. J. Karvanen and V. Koivunen, Blind separation methods based on pearson system and its extensions, Signal Processing, vol.82, no.4, pp.663-673, 2002.
9. R. Boscolo, H. Pan and V. P. Roychowdhury, Independent component analysis based on nonparametric density estimation, IEEE Transaction on Neural Networks, vol.15, no.1, pp.55-65, 2004.
10. F. S. Wang, H. W. Li, R. Li and Y. J. Zhang, Novel ICA algorithm with nonparametric estimation based on GGD kernel, International Journal of Innovative Computing, Information and Control, vol.2, no.2, pp.427-440, 2006.
11. F. S. Wang and H. W. Li, Adaptive ICA algorithm based on asymmetric generalized Gaussian density model, Proc. of the 7th International Conference on Adaptive and Natural Computing Algorithms, Coimbra, Portugal, Springer Press, pp.498-501, 2005.
12. S. Amari, Natural gradient works efficiently in learning, Neural Computation, vol.10, no.2, pp.251-276, 1998.
13. J. Cao, N. Murata, S. Amari, A. Cichocki and T. Takeda, A robust Aapproach to independent component analysis of signals with high-level noise measurements, IEEE Transaction on Neural Network vol.14, no.3, pp.631-645, 2003.
14. A. Tesei and C. S. Regazzoni, HOS-based generalized noise pdf models for signal detection optimization, Signal Processing, vol.65, no.2, pp.267-281, 1998.
15. S. Amari, T. Chen and A. Cichocki, Stability analysis of adaptive blind source separation, Neural Networks, vol.8, no.10, pp.1345-1351, 1997.
16. B. W. Silverman, Density Estimation for Statistics and Data Analysis, Chapman and Hall, New York, 1985.
17. F. R. Bach and M. I. Jordan, Kernel independent component analysis, Journal of Machine Learning Research, vol.3, no.1, pp.1-48, 2002.
18. H. Zhang, M. Basin and M. Skliar, Optimal state estimation for continuous, stochastic, state-space system with hybrid measurements, Int. J. Innovative Computing, Information and Control, vol.2, no.2, pp.357-370, 2006.
19. I. Xie, H. Ogai, Y. Inoe and A. Ohata, Numerical solving of hybrid dynamic switching system and its application, Int. J. Innovative Computing, Information and Control, vol.2, no.4, pp.849-862, 2006.