Bayesian k-means as a " maximization-expectation" algorithm

Neural Computation

Volumn 21, Issue 4, 2009, Pages 1145-1172

  Kurihara, Kenichi ^a   Welling, Max ^b  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^b Irvine   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BIOLOGICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; MODELS, NEUROLOGICAL;

EID: 65549098786     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2008.12-06-421     Document Type: Letter

References (17)

1. Attias, H. (2000). A variational Bayesian framework for graphical models. In S. A. Solla, T. K. Leen, & K.-R. Müller (Eds.), Advances in neural information processing systems, 12. Cambridge, MA:MIT Press.
2. Dasgupta, S. (2000). Experiments with random projection. In Proceedings of the 16th Annual Conference on Uncertainty in Artificial Intelligence (pp. 143-151). San Francisco: Morgan Kaufmann.
3. Elkan, C. (2003). Using the triangle inequality to accelerate k-means. In Proc. 20 International Conf. on Machine Learning (pp. 147-153). Menlo Park, CA: AAAI Press.
4. Eppstein, D. (1998). Fast hierarchical clustering and other applications of dynamic closest pairs. In SODA: ACM-SIAM Symposium on Discrete Algorithms. Available online at citeseer.ist.psu.edu/eppstein98fast.html.
5. Ghahramani, Z., & Beal, M. J. (2000). Variational inference for Bayesian mixtures of factor analysers. In S. A. Solla, T. K. Leen, & K.-R. Müller (Eds.), Advances in neural information processing systems, 12. Cambridge, MA: MIT Press.
6. Griffiths, T., & Ghahramani, Z. (2006). Infinite latent feature models and the Indian buffet process. In Y. Weiss, B. Schölkopf, & J. Platt (Eds.), Advances in neural information processing systems, 18 (pp. 475-482). Cambridge, MA: MIT Press.
7. Hamerly, G., & Elkan, C. (2003). Learning the k in k-means. In L. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in neural information processing systems, 17. Cambridge, MA: MIT Press.
8. Hinton, U., & van Camp, G. (1993). Keeping neural networks simple by minimizing the description length of the weights. In Proc. 6th Annual Workshop on Comput. Learning Theory (pp. 5-13). New York: ACM Press.
9. MacEachern, S. N., & Müller, P. (1998). Estimating mixture of Dirichlet process models. Communications in Statistics, 7, 223-238.
10. Moore, A. (1998). Very fast EM-based mixture model clustering using multiresolution kd-trees. In M. I. Jordan, M. J. Kearns, & S. A. Solla (Eds.), Advances in neural information processing systems, 10. Cambridge, MA: MIT Press.
11. Moore, A. (2000). The anchors hierarchy: Using the triangle inequality to survive high-dimensional data. In Proc. of the 12th Conf. on Uncertainty in Artificial Intelligence (pp. 397-405). Menlo Park, CA: AAAI Press.
12. Pelleg, D., & Moore, A. (1999). Accelerating exact k-means algorithms with geometric reasoning. In Proc. of the 5th Int'l Conf. on Knowledge Discovery in Databases (pp. 277-281). Menlo Park, CA: AAAI Press.
13. Pelleg, D., & Moore, A. (2000). X-means: Extending K-means with efficient estimation of the number of clusters. In Proceedings of the 17th International Conference on Machine Learning (pp. 727-734). San Francisco: Morgan Kaufmann.
14. Shannon, C. E. (1948). A mathematical theory of communication. Bell Sys. Tech. Journal, 27, 379-423, 623-656.
15. Ueda, N., Nakano, R., Ghahramani, Z., & Hinton, G. E. (2000). SMEM algorithm for mixture models. Neural Computation, 12(9), 2109-2128.
16. Verbeek, J., Nunnink, J., & Vlassis, N. (2003). Accelerated variants of the EM algorithm for gaussian mixtures (Tech. Rep.) Amsterdam: University of Amsterdam.
17. Zhang, T., Ramakrishnan, R., & Livny, M. (1996). Birch: An efficient data clustering method for very large databases. In ACM-SIGMOD Int. Conf. Management of Data (pp. 103-114). New York: ACM Press.