Greedy learning of binary latent trees

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 33, Issue 6, 2011, Pages 1087-1097

  Harmeling, Stefan ^a   Williams, Christopher K I ^b  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^b UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

greedy methods; hierarchical latent class model; latent variable model; Unsupervised learning

Indexed keywords

AGGLOMERATIVE HIERARCHICAL CLUSTERING; BAYESIAN NETWORK STRUCTURE; BOTTOM-UP FASHION; CARDINALITIES; DATA SETS; GREEDY LEARNING; GREEDY METHOD; HIERARCHICAL LATENT CLASS MODELS; LATENT STRUCTURES; LATENT VARIABLE; LATENT VARIABLE MODEL; LOG LIKELIHOOD; NEWSGROUPS; REAL WORLD DATA; REPRODUCIBILITIES; SEARCH ALGORITHMS; STRUCTURED MODEL; TOPIC MODEL; TREE STRUCTURES; VISUAL OBJECTS;

BAYESIAN NETWORKS; BINARY TREES; BINS; CLUSTERING ALGORITHMS; EQUIVALENCE CLASSES; INFERENCE ENGINES; MODELS; MULTI AGENT SYSTEMS; REAL VARIABLES; UNSUPERVISED LEARNING;

TREES (MATHEMATICS);

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CLUSTER ANALYSIS; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CLUSTER ANALYSIS; MODELS, THEORETICAL;

EID: 79955471570     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2010.145     Document Type: Article

References (22)

1. D. Blei, A. Ng, and M. Jordan, "Latent Dirichlet Allocation," J. Machine Learning Research, vol. 3, pp. 993-1022, 2003.
2. K.A. Bollen, Structural Equations with Latent Variables. Wiley, 1989.
3. C.K. Chow and C.N. Liu, "Approximating Discrete Probability Distributions with Dependence Trees," IEEE Trans. Information Theory, vol. 14, no. 3, pp. 462-467, May 1968.
4. D. Connolly, "Constructing Hidden Variables in Bayesian Networks via Conceptual Clustering," Proc. 10th Int'l Conf. Machine Learning, pp. 65-72, 1993.
5. R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis. Wiley, 1973.
6. J. Felsenstein, Inferring Phylogenies. Sinauer Assoc, 2004.
7. X. Feng, C.K.I. Williams, and S.N. Felderhof, "Combining Belief Networks and Neural Networks for Scene Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 467-483, Apr. 2002. (Pubitemid 34448766)
8. D.H. Fisher, "Knowledge Acquisition via Incremental Conceptual Clustering," Machine Learning, vol. 2, pp. 139-172, 1987.
9. N. Friedman, "Pcluster: Probabilistic Agglomerative Clustering of Gene Expression Profiles," Technical Report 80, Hebrew Univ., 2003.
10. K. Heller and Z. Ghahramani, "Bayesian Hierarchical Clustering," Proc. 22nd Int'l Conf. Machine Learning, pp. 297-304, 2005. (Pubitemid 43183346)
11. G.E. Hinton, S. Osindero, and YW. Teh, "A Fast Learning Algorithm for Deep Belief Nets," Neural Computation, vol. 18, pp. 1527-1554, 2006. (Pubitemid 44024729)
12. C. Kemp and J.B. Tenenbaum, "The Discovery of Structural Form," Proc. Nat'l Academy of Sciences, vol. 105, pp. 10687-10692, 2008.
13. T. Kohlmann and A.K. Formann, "Using Latent Class Models to Analyze Response Patterns in Epidemiologic Mail Surveys," Applications of Latent Trait and Latent Class Models in the Social Sciences, J. Rost and R. Langeheine, eds., Waxman Verlag, 1997.
14. I. Kojadinovic, "Agglomerative Hierarchical Clustering of Continuous Variables Based on Mutual Information," Computational Statistics and Data Analysis, vol. 46, pp. 269-294, 2004. (Pubitemid 38503275)
15. P.F. Lazarsfeld and NW. Henry, Latent Structure Analysis. Houghton Mifflin, 1968.
16. R.M. Neal, "Density Modeling and Clustering Using Dirichlet Diffusion Trees," Bayesian Statistics, J.M. Bernardo et al., eds., vol. 7, pp. 619-629, Oxford Univ. Press, 2003.
17. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
18. Y.W. Teh, H. Daume, III, and D.M. Roy, "Bayesian Agglomerative Clustering with Coalescents," Advances in Neural Information Processing Systems, J. Platt, D. Koller, Y. Singer, and S. Roweis, eds., vol. 20, MIT Press, 2008.
19. Y. Wang, N. Zhang, and T. Chen, "Latent Tree Models and Approximate Inference in Bayesian Networks," J. Artificial Intelligence Research, vol. 32, pp. 879-900, 2008.
20. C.K.I. Williams, "A MCMC Approach to Hierarchical Mixture Modelling," Advances in Neural Information Processing Systems, S.A. Solla, T.K. Leen, and K.-R Muller, eds., vol. 12, MIT Press, 2000.
21. N.L. Zhang, "Hierachical Latent Class Models for Cluster Analysis," J. Machine Learning Research, vol. 5, pp. 697-723, 2004.
22. N.L. Zhang and T. Kocka, "Efficient Learning of Hierarchical Latent Class Models," Proc. 16th IEEE Int'l Conf. Tools with AI, 2004.