Latent Dirichlet allocation

Journal of Machine Learning Research

Volumn 3, Issue 4-5, 2003, Pages 993-1022

  Blei, David M ^a   Ng, Andrew Y ^b   Jordan, Michael I ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBABILITY; TEXT PROCESSING; VARIATIONAL TECHNIQUES;

BAYESIAN MODEL; COLLABORATIVE FILTERING; LATENT DIRICHLET ALLOCATION; PROBABILISTIC MODEL; TEXT CLASSIFICATION;

LEARNING SYSTEMS;

EID: 0141607824     PISSN: 15324435     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (32)

1. M. Abramowitz and I. Stegun, editors. Handbook of Mathematical Functions. Dover, New York, 1970.
2. D. Aldous. Exchangeability and related topics. In École d'été de probabilités de Saint-Flour, XIII-1983, pages 1-198, Springer, Berlin, 1985.
3. H. Attias. A variational Bayesian framework for graphical models. In Advances in Neural Information Processing Systems 12, 2000.
4. L. Avery. Caenorrhabditis genetic center bibliography. 2002. URL http://elegans.swmed.edu/wli/cgcbib
5. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. ACM Press, New York, 1999.
6. D. Blei and M. Jordan. Modeling annotated data. Technical Report UCB//CSD-02-1202, U.C. Berkeley Computer Science Division, 2002.
7. B. de Finetti. Theory of probability. Vol. 1-2, John Wiley & Sons Ltd., Chichester, 1990. Reprint of the 1975 translation.
8. S. Deerwester, S. Dumais, T. Landauer, G. Furnas, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6):391-407, 1990.
9. P. Diaconis. Recent progress on de Finetti's notions of exchangeability. In Bayesian statistics, 3 (Valencia, 1987), pages 111-125 Oxford Univ. Press, New York, 1988.
10. J. Dickey. Multiple hypergeometric functions: Probabilistic interpretations and statistical uses. Journal of the American Statistical Association, 78:628-637, 1983.
11. J. Dickey, J. Jiang, and J. Kadane. Bayesian methods for censored categorical data. Journal of the American Statistical Association, 82:773-781, 1987.
12. A. Gelman, J. Carlin, H. Stern, and D. Rubin. Bayesian data analysis. Chapman & Hall, London, 1995.
13. T. Griffiths and M. Steyvers. A probabilistic approach to semantic representation. In Proceedings of the 24th Annual Conference of the Cognitive Science Society, 2002.
14. D. Harman. Overview of the first text retrieval conference (TREC-1). In Proceedings of the First Text Retrieval Conference (TREC-1), pages 1-20, 1992.
15. D. Heckerman and M. Meila. An experimental comparison of several clustering and initialization methods. Machine Learning, 42:9-29, 2001.
16. T. Hofmann. Probabilistic latent semantic indexing. Proceedings of the Twenty-Second Annual International SIGIR Conference, 1999.
17. F. Jelinek. Statistical Methods for Speech Recognition. MIT Press, Cambridge, MA, 1997.
18. T. Joachims. Making large-scale SVM learning practical. In Advances in Kernel Methods - Support Vector Learning. M.I.T. Press, 1999.
19. M. Jordan, editor. Learning in Graphical Models. MIT Press, Cambridge, MA, 1999.
20. M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul. Introduction to variational methods for graphical models. Machine Learning, 37:183-233, 1999.
21. R. Kass and D. Steffey. Approximate Bayesian inference in conditionally independent hierarchical models (parametric empirical Bayes models). Journal of the American Statistical Association, 84 (407):717-726, 1989.
22. M. Leisink and H. Kappen. General lower bounds based on computer generated higher order expansions. In Uncertainty in Artificial Intelligence, Proceedings of the Eighteenth Conference, 2002.
23. T. Minka. Estimating a Dirichlet distribution. Technical report, M.I.T., 2000.
24. T. P. Minka and J. Lafferty. Expectation-propagation for the generative aspect model. In Uncertainty in Artificial Intelligence (UAI), 2002.
25. C. Morris. Parametric empirical Bayes inference: Theory and applications. Journal of the American Statistical Association, 78(381):47-65, 1983. With discussion.
26. K. Nigam, J. Lafferty, and A. McCallum. Using maximum entropy for text classification. IJCAI-99 Workshop on Machine Learning for Information Filtering, pages 61-67, 1999.
27. K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2/3):103-134, 2000.
28. C. Papadimitriou, H. Tamaki, P. Raghavan, and S. Vempala. Latent semantic indexing: A probabilistic analysis. pages 159-168, 1998.
29. A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Uncertainty in Artificial Intelligence, Proceedings of the Seventeenth Conference, 2001.
30. J. Rennie. Improving multi-class text classification with naive Bayes. Technical Report AITR-2001-004, M.I.T., 2001.
31. G. Ronning. Maximum likelihood estimation of Dirichlet distributions. Journal of Statistical Computation and Simulation, 34(4):215-221, 1989.
32. G. Salton and M. McGill, editors. Introduction to Modern Information Retrieval. McGraw-Hill, 1983.