On the effect of the form of the posterior approximation in variational learning of ICA models

Neural Processing Letters

Volumn 22, Issue 2, 2005, Pages 183-204

  Ilin, Alexander ^a   Valpola, Harri ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

Independent component analysis; Variational Bayesian learning

Indexed keywords

APPROXIMATION THEORY; LEARNING SYSTEMS; LINEAR SYSTEMS; MATHEMATICAL MODELS; VARIATIONAL TECHNIQUES; VECTORS;

APPROXIMATION AFFECTS; NONLINEAR MIXTURES; VARIATIONAL BAYESIAN LEARNING;

INDEPENDENT COMPONENT ANALYSIS;

EID: 25444480219     PISSN: 13704621     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11063-005-5265-0     Document Type: Article

References (22)

1. Attias, H.: Independent factor analysis, Neural Computation 11(4) (1999), 803-851.
2. Lappalainen, H.: Ensemble learning for independent component analysis, In: Proceedings of Int. Workshop on Independent Component Analysis and Signal Separation (ICA99), pp. 7-12, Aussois: France, 1999.
3. Miskin, J. and MacKay, D. J. C.: Ensemble learning for blind source separation, In: S. Roberts and R. Everson (eds.),Independent Component Analysis: Principles and Practice, pp. 209-233, Cambridge University Press, 2001.
4. Choudrey, R., Penny, W. and Roberts, S.: An ensemble learning approach to independent component analysis, In: Proceeding of the IEEE Workshop on Neural Networks for Signal Processing, pp. 435-444, IEEE Press: Sydney, Australia, 2000.
5. Valpola, H.: Nonlinear independent component analysis using ensemble learning: theory, In: Proceeding of International Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000), pp. 251-256, Helsinki, Finland, 2000.
6. Chan, K., Lee, T. and Sejnowski, T. J.: Variational learning of clusters of undercomplete nonsymmetric independent components, Journal of Machine Learning Research 3 (2002), 99-114.
7. Chan, K., Lee, T. and Sejnowski, T. J.: Variational bayesian learning of ICA with missing data, Neural Computation 15(8) (2003), 1991-2011.
8. Valpola, H. and Karhunen, J.: An unsupervised ensemble learning method for nonlinear dynamic state-space models, Neural computation 14(11) (2002), 2647-2692.
9. Ilin, A. and Valpola, H.: On the effect of the form of the posterior approximation in variational learning of ICA models, In: Proceeding of the 4th International Conference on Independent Component Analysis and Blind Signal Separation (ICA2003), pp. 915-920, Nara: Japan, 2003.
10. Barber, D. and Bishop, C. Ensemble learning for multi-layer networks, In: M. Jordan, M. Kearns and S. Solla (eds.), Advances in Neural Information Processing Systems 10, pp. 395-401, The MIT Press: Cambridge, MA, USA.
11. Ghahramani, Z. and Hinton, G. E.: Variational learning for switching state-space models, Neural computation 12(4) (2000), 963-996.
12. Hyvärinen, A., Karhunen, J. and Oja, E.: Independent Component Analysis, Wiley, 2001.
13. Belouchrani, A., Meraim, K. A., Cardoso, J.-F. and Moulines, E.: A blind source separation technique based on second order statistics, IEEE Trans. on Signal Processing 45(2) (1997), 434-444.
14. Ziehe, A., Müller, K.-R., Nolte, G., Mackert, B.-M. and Cuiro, G.: Artifact reduction in manetoneurography based on time-delayed second order correlations, Technical Report 31, GMD-Forschungszentrum Informationstechnik GmbH, 1998.
15. Tong, L., Soo, V., Liu, R. and Huang, Y.: Amuse: a new blind identification algorithm, In: Proceedings ISCAS, New Orleans, USA, 1990.
16. Ghahramani, Z. and Beal, M.: Propagation algorithms for variational Bayesian learning, In: T. Leen, T. Dietterich and V. Tresp (eds.), Advances in Neural Information Processing Systems 13, pp. 507-513, The MIT Press: Cambridge, MA, USA, 2001.
17. Valpola, H., Honkela, A., Harva, M., Hin, A., Raiko, T. and Östman, T.: Bayes blocks software library, http://www.cis.hut.fi/projects/bayes/sofware/ , 2003.
18. Valpola, H., Raiko, T. and Karhunen, J.: Building blocks for hierarchical latent variable models, In: Proceedings of the 3rd International Conference on Independent Component Analysis and Signal Separation (ICA2001), pp. 710-715, San Diego, USA, 2001.
19. Ilin A.: Matlab code for variational bayesian learning of linear ICA models, http://www.cis.hut.fi/projects/bayes/sofware/np12004/, 2004.
20. Valpola, H., Harva, M. and Karhunen, J.: Hierarchical models of variance sources, Signal Processing 84(2) (2004), 267-282.
21. Harva, M.: Hierarchical variance models of image sequences. Master's thesis, Helsinki University of Technology: Espoo, 2004.
22. Højen-Sørensen, P. A. d. F. R., Winter, O. and Hansen, L. K.: Mean-field approaches to independent component analysis, Neural Computation 14(4) (2002), 889-918.