Super-Gaussian mixture source model for ICA

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3889 LNCS, Issue , 2006, Pages 854-861

  Palmer, Jason A ^a,b   Kreutz Delgado, Kenneth ^a   Makeig, Scott ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

NON-GAUSSIAN MIXTURE MODELS; SUPER-GAUSSIAN DENSITIES; SUPER-GAUSSIAN MIXTURE SOURCE MODEL;

ALGORITHMS; APPROXIMATION THEORY; INDEPENDENT COMPONENT ANALYSIS; MAXIMUM LIKELIHOOD ESTIMATION;

BLIND SOURCE SEPARATION;

EID: 33745711477     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11679363_106     Document Type: Conference Paper

References (19)

1. S.-I. Amari. Natural gradient works efficiently in learning. Neural Computation, 10(2):251-276, 1998.
2. H. Attias. Independent factor analysis. Neural Computation, 11:803-851, 1999.
3. H. Attias. A variational Bayesian framework for graphical models. In Advances in Neural Information Processing Systems 12. MIT Press, 2000.
4. A. Benveniste, M. Goursat, and G. Ruget. Robust identification of a nonminimum phase system. IEEE Transactions on Automatic Control, 25(3):385-399, 1980.
5. K. Chan, T.-W. Lee, and T. J. Sejnowski. Variational learning of clusters of undercomplete nonsymmetric independent components. Journal of Machine Learning Research, 3:99-114, 2002.
6. R. A. Choudrey and S. J. Roberts. Variational mixture of Bayesian independent component analysers. Neural Computation, 15(1):213-252, 2002.
7. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39:1-38, 1977.
8. Z. Ghahramani and M. J. Beal. Variational inference for Bayesian mixtures of factor analysers. In Advances in Neural Information Processing Systems 12. MIT Press, 2000.
9. M. Girolami. A variational method for learning sparse and overcomplete representations. Neural Computation, 13:2517-2532, 2001.
10. T. S. Jaakkola. Variational Methods for Inference and Estimation in Graphical Models. PhD thesis, Massachusetts Institute of Technology, 1997.
11. T. S. Jaakkola and M. I. Jordan. A variational approach to Bayesian logistic regression models and their extensions. In Proceedings of the 1997 Conference on Artificial Intelligence and Statistics, 1997.
12. T.-W. Lee, M. S. Lewicki, and T. J. Sejnowski. ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation. IEEE Trans. Pattern Analysis and Machine Intelligence, 22(10): 1078-1089, 2000.
13. D. J. C. Mackay. Comparison of approximate methods for handling hyperparameters. Neural Computation, 11(5):1035-1068, 1999.
14. R. M. Neal and G. E. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. In M. I. Jordan, editor, Learning in Graphical Models, pages 355-368. Kluwer, 1998.
15. J. A. Palmer, K. Kreutz-Delgado, D. P. Wipf, and B. D. Rao. Variational EM algorithms for non-gaussian latent variable models. In Advances in Neural Information Processing Systems. MIT Press, 2005. Available at http://dsp.ucsd.edu/~japalmer/.
16. H.-J. Park and T.-W. Lee. Modeling nonlinear dependencies in natural images using mixture of Laplacian distribution. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 14, Cambridge, MA, 2004. MIT Press.
17. B. A. Pearlmutter and L. C. Parra. Maximum likelihood blind source separation: A context-sensitive generalization of ICA. In M. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems. MIT Press, 1996.
18. L. K. Saul, T. S. Jaakkola, and M. I. Jordan. Mean field theory for sigmoid belief networks. Journal of Artificial Intelligence Research, 4:61-76, 1996.
19. M. E. Tipping. Sparse Bayesian learning and the Relevance Vector Machine. Journal of Machine Learning Research, 1:211-244, 2001.