Maximum marginal likelihood estimation for nonnegative dictionary learning

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2011, Pages 1992-1995

  Dikmen, Onur ^a   Fevotte, Cedric ^a  

^a INSTITUT TELECOM   (France)

Author keywords

automatic relevance determination; model order selection; Nonnegative matrix factorisation; sparse coding; variational EM

Indexed keywords

AUTOMATIC RELEVANCE DETERMINATION; MODEL ORDER SELECTION; NON-NEGATIVE MATRIX; SPARSE CODING; VARIATIONAL EM;

ALGORITHMS; ESTIMATION; EXPANSION; FACTORIZATION; SIGNAL PROCESSING; SPEECH COMMUNICATION;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 80051609022     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2011.5946901     Document Type: Conference Paper

References (13)

1. D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization," Nature, vol. 401, pp. 788-791, 1999.
2. M. S. Lewicki and T. J. Sejnowski, "Learning overcomplete representations," Neural Computation, vol. 12, pp. 337-365, 2000.
3. M. N. Schmidt, O.Winther, and L. K. Hansen, "Bayesian nonnegative matrix factorization," in Proc. 8th International Conference on Independent Component Analysis and Signal Separation (ICA'09), Paraty, Brazil, Mar. 2009.
4. C. Févotte, N. Bertin, and J.-L. Durrieu, "Nonnegative matrix factorization with the Itakura-Saito divergence. With application to music analysis," Neural Computation, vol. 21, no. 3, pp. 793-830, Mar. 2009.
5. C. Févotte and A. T. Cemgil, "Nonnegative matrix factorisations as probabilistic inference in composite models," in Proc. 17th European Signal Processing Conference (EUSIPCO' 09), Glasgow, Scotland, Aug. 2009, pp. 1913-1917.
6. J. F. Canny, "GaP: A factor model for discrete data," in Proc. of the 27th ACM international Conference on Research and Development of Information Retrieval (SIGIR), 2004, pp. 122-129.
7. W. L. Buntine and A. Jakulin, "Discrete component analysis," in Subspace, Latent Structure and Feature Selection Techniques, 2005, pp. 1-33.
8. A. T. Cemgil, "Bayesian inference for nonnegative matrix factorisation models," Computational Intelligence and Neuroscience, vol. 2009, no. Article ID 785152, pp. 17 pages, 2009.
9. M. J. Beal and Z. Ghahramani, "The variational Bayesian EM algorithm for incomplete data: with application to scoring graphical model structures," in Bayesian Statistics 7, Oxford University Press, 2003.
10. S. Chib, "Marginal likelihood from the Gibbs output," Journal of the American Statistical Association, vol. 90, no. 432, pp. 1313-1321, 1995.
11. D. J. C. MacKay, "Probable networks and plausible predictions - a review of practical Bayesian models for supervised neural networks," Network: Computation in Neural Systems, vol. 6, no. 3, pp. 469-505, 1995.
12. C. M. Bishop, "Bayesian PCA," in Advances in Neural Information Processing Systems (NIPS), pp. 382-388, 1999.
13. D. Donoho and V. Stodden, "When does non-negative matrix factorization give a correct decomposition into parts?," in Advances in Neural Information Processing Systems (NIPS), 2004.