Bayesian independent component analysis: Variational methods and non-negative decompositions

Digital Signal Processing: A Review Journal

Volumn 17, Issue 5, 2007, Pages 858-872

  Winther, Ole ^a   Petersen, Kaare Brandt ^a  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SOFTWARE; MATRIX ALGEBRA; OPTIMIZATION; PARAMETER ESTIMATION; SENSORS;

COST FUNCTION; EXPECTATION MAXIMIZATION (EM); MATLAB TOOLBOX; VARIATIONAL BAYES;

INDEPENDENT COMPONENT ANALYSIS;

EID: 34547601456     PISSN: 10512004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dsp.2007.01.003     Document Type: Article

References (20)

1. A. Belouchrani, J.-F. Cardoso, A maximum likelihood source separation for discrete sources, in: Proceedings of EUSIPCO, vol. 2, 1994, pp. 768-771
2. E. Moulines, J.-F. Cardoso, E. Gassiat, Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models, in: Proceedings of the ICA Conference, 1997
3. Miskin J.W., and McKay D. Ensemble learning for blind source separation. In: Roberts S., and Everson R. (Eds). Independent Component Analysis: Principles and Practice (2001), Cambridge Univ. Press, Cambridge
4. Hojen-Sorensen P., Winther O., and Hansen L.K. Mean-field approaches to independent component analysis. Neural Comput. 14 (2002) 889-918
5. O. Bermond, J.-F. Cardoso, Approximate likelihood for noisy mixtures, in: Proceedings of the ICA Conference, 1999
6. Petersen K.B., Winther O., and Hansen L.K. On the convergence of EM and VBEM. Neural Comput. 17 (2005) 1921-1926
7. R. Salakhutdinov, S. Roweis, Adaptive overrelaxed bound optimization methods, in: Proceedings of International Conference on Machine Learning, ICML, 2003
8. Olsson R.K., Petersen K.B., and Lehn-Schioeler T. State-space models-From the EM algorithm to a gradient approach. Neural Comput. 19 4 (2007)
9. Lee D., and Seung H. Learning the parts of objects by non-negative matrix factorization. Nature 401 (1999) 788-791
10. Lee D.D., and Seung H.S. Algorithms for non-negative matrix factorization. In: Leen T.K., Dietterich T.G., and Tresp V. (Eds). Advances in Neural Information Processing Systems (NIPS) vol. 13 (2001) 556-562
11. Attias H. A variational Bayesian framework for graphical models. In: Leen T., Dietterich T.G., and Tresp V. (Eds). Advances in Neural Information Processing Systems vol. 12 (2000), MIT Press, Cambridge 209-215
12. Jordan M.I., Ghahramani Z., Jaakkola T.S., and Saul L.K. An introduction to variational methods for graphical models. Mach. Learn. 37 (1999) 183-233
13. Opper M., and Winther O. Expectation consistent approximate inference. J. Mach. Learn. Res. 6 (2005) 2177-2204
14. O. Winther, K.B. Petersen, Flexible and efficient implementations of Bayesian independent component analysis, Neurocomputing (2007), submitted for publication
15. Opper M., and Winther O. Gaussian processes for classification: Mean field algorithms. Neural Comput. 12 (2000) 2655-2684
16. Opper M., and Winther O. Adaptive and self-averaging Thouless-Anderson-Palmer mean field theory for probabilistic modeling. Phys. Rev. E 64 (2001) 056131
17. T. Minka, Expectation Propagation for Approximate Bayesian Inference, Doctoral dissertation, MIT Media Lab., 2001
18. T.P. Minka, Divergence measures and message passing, Microsoft Tech. Report, 2005
19. Pontoppidan N.H., Sigurdsson S., and Larsen J. Condition monitoring with mean field independent components analysis. Mech. Syst. Signal Process. 19 6 (2005) 1337-1347
20. Hinton G.E., Osindero S., and Teh Y.W. A fast learning algorithm for deep belief networks. Neural Comput. 18 (2006) 1527-1554