Learning with blocks: Composite likelihood and contrastive divergence

Journal of Machine Learning Research

Volumn 9, Issue , 2010, Pages 33-40

  Asuncion, Arthur U ^a   Liu, Qiang ^a   Ihler, Alexander T ^a   Smyth, Padhraic ^a  

^a Irvine   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BOLTZMANN MACHINES; COMPOSITE LIKELIHOOD; COMPUTATION TIME; COMPUTATIONALLY EFFICIENT; CONDITIONAL RANDOM FIELD; CONTRASTIVE DIVERGENCE; EXPONENTIAL RANDOM GRAPH MODEL; GIBBS SAMPLING; HIGHER ORDER; LEARNING FRAMEWORKS; MODEL SELECTION; PARAMETER ESTIMATE; RATE OF CONVERGENCE; TRADE OFF; WIDE SPECTRUM;

APPROXIMATION THEORY; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL EFFICIENCY; GRAPH THEORY; PARAMETER ESTIMATION;

OPTIMIZATION;

EID: 77956538641     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (23)

1. Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society. Series B, 36(2):192-236.
2. Carreira-Perpinãń, M. Á. and Hinton, G. (2005). On contrastive divergence learning. In 10th International Conference on AI and Statistics, pages 33-40.
3. Dillon, J. and Lebanon, G. (2009). Statistical and computational tradeoffs in stochastic composite likelihood. In 12th International Conference on AI and Statistics, pages 129-136.
4. Fearnhead, P. and Donnelly, P. (2002). Approximate likelihood methods for estimating local recombination rates. Journal of the Royal Statistical Society. Series B, 64(4):657-680.
5. Geyer, C. J. (1991). Markov chain Monte Carlo maximum likelihood. Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface, pages 156-163.
6. Handcock, M., Hunter, D., Butts, C., Goodreau, S., and Morris, M. (2008). statnet: Software tools for the representation, visualization, analysis and simulation of network data. Journal of Statistical Software, 24(1):1548.
7. Hinton, G. (2000). Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):1771-1800.
8. Hinton, G. and Salakhutdinov, R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786):504.
9. Hyvärinen, A. (2006). Consistency of pseudolikelihood estimation of fully visible Boltzmann machines. Neural Computation, 18(10):2283-2292.
10. Hyvärinen, A. (2007). Connections between score matching, contrastive divergence, and pseudolikelihood for continuousvalued variables. IEEE Transactions on Neural Networks, 18(5):1529-1531.
11. Kumar, S. and Hebert, M. (2004). Discriminative fields for modeling spatial dependencies in natural images. In Advances in Neural Information Processing Systems 16, pages 1531 - 1538, Cambridge, MA. MIT Press.
12. Lehmann, E. L. and Casella, G. (1998). Theory of point estimation. Springer Verlag.
13. Liang, P. and Jordan, M. I. (2008). An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. In 25th International Conference on Machine Learning, pages 584-591, New York. ACM.
14. Lindsay, B. G. (1988). Composite likelihood methods. Contemporary Mathematics, 80(1):22139.
15. Parner, E. (2001). A composite likelihood approach to multivariate survival data. Scandinavian Journal of Statistics, 28(2):295-302.
16. Robins, G., Pattison, P., Kalish, Y., and Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2):173-191.
17. Snijders, T. A. B. (2002). Markov chain Monte Carlo estimation of exponential random graph models. Journal of Social Structure, 3(2):1-40.
18. Snijders, T. A. B., Pattison, P. E., Robins, G. L., and Handcock, M. S. (2006). New specifications for exponential random graph models. Sociological Methodology, pages 99-153.
19. Strauss, D. and Ikeda, M. (1990). Pseudolikelihood estimation for social networks. Journal of the American Statistical Association, 85(409):204-212.
20. Tieleman, T. and Hinton, G. (2009). Using fast weights to improve persistent contrastive divergence. In 26th International Conference on Machine Learning, pages 1033-1040, New York. ACM.
21. van Duijn, M. A., Gile, K. J., and Handcock, M. S. (2009). A framework for the comparison of maximum pseudo-likelihood and maximum likelihood estimation of exponential family random graph models. Social Networks, 31(1):52-62.
22. Varin, C. and Vidoni, P. (2005). A note on composite likelihood inference and model selection. Biometrika, 92(3):519-528.
23. Vishwanathan, S., Schraudolph, N., Schmidt, M., and Murphy, P. (2006). Accelerated training of conditional random fields with stochastic gradient methods. In 23rd International Conference on Machine Learning, pages 969-976, New York. ACM.