Piecewise bounds for estimating bernoulli-logistic latent Gaussian models

Proceedings of the 28th International Conference on Machine Learning, ICML 2011

Volumn , Issue , 2011, Pages 633-638

  Marlin, Benjamin M ^a   Khan, Mohammad Emtiyaz ^a   Murphy, Kevin P ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EMPIRICAL RESULTS; GAUSSIAN MODEL; GENERALIZED EXPECTATION MAXIMIZATION; MARGINAL LIKELIHOOD; MAXIMUM ERROR; MINIMAX; PIECE-WISE; PIECEWISE LINEAR; QUADRATIC BOUND; THEORETICAL RESULT; UPPER BOUND; VARIATIONAL BOUNDS;

ALGORITHMS; GAUSSIAN DISTRIBUTION; LEARNING SYSTEMS; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; PIECEWISE LINEAR TECHNIQUES;

ESTIMATION;

EID: 80053441013     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Bishop, C. Pattern recognition and machine learning. Springer, 2006.
2. Bohning, D. Multinomial logistic regression algorithm. Annals of the Institute of Statistical Mathematics, 44:197-200, 1992.
3. Collins, M., Dasgupta, S., and Schapire, R.E. A generalization of principal component analysis to the exponential family. In Advances in neural information processing systems, pp. 617-624, 2002.
4. Dempster, A. P., Laird, N. M., and Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 34:1-38, 1977.
5. Hsiung, K., Kim, S., and Boyd, S. Tractable approximate robust geometric programming. Optimization and Engineering, 9:95-118, 2008. (Pubitemid 351460135)
6. Jaakkola, T. and Jordan, M. A variational approach to Bayesian logistic regression problems and their extensions. In Proceedings of the International Conference on Artificial Intelligence and Statistics, 1996.
7. Khan, M.E., Marlin, B.M., Bouchard, G., and Murphy, K. Variational bounds for mixed-data factor analysis. In Advances in Neural Information Proceeding Systems, 2010.
8. Kuss and Rasmussen, C. Assessing approximate inference for binary gaussian process classification. Journal of Machine Learning Research, 6:1679-1704, 2005.
9. Minka, T. Expectation propagation for approximate Bayesian inference. In UAI, 2001.
10. Mohamed, S., Heller, K., and Ghahramani, Z. Bayesian Exponential Family PCA. In Advances in Neural Information Proceeding Systems, 2008.
11. Neider, J.A. and Mead, R. A simplex method for function minimization. The computer journal, 7(4):308, 1965.
12. Tipping, M. and Bishop, C. Probabilistic principal component analysis. Journal of Royal Statistical Society Series B, 21(3):611-622, 1999.
13. Wedel, Michel and Kamakura, Wagner. Factor analysis with (mixed) observed and latent variables in the exponential family. Psychometrika, 66(4):515-530, December 2001.