Latent dirichlet allocation | The Journal of Machine Learning Research

Abstract

References

1. M. Abramowitz and I. Stegun, editors. Handbook of Mathematical Functions. Dover, New York, 1970. Google Scholar
2. D. Aldous. Exchangeability and related topics. In École d'été de probabilités de Saint-Flour, XIII-- 1983, pages 1-198. Springer, Berlin, 1985.Google Scholar
3. H. Attias. A variational Bayesian framework for graphical models. In Advances in Neural Information Processing Systems 12, 2000.Google Scholar
4. L. Avery. Caenorrhabditis genetic center bibliography. 2002. URL http://elegans.swmed.edu/wli/cgcbib.Google Scholar
5. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. ACM Press, New York, 1999. Google Scholar
6. D. Blei and M. Jordan. Modeling annotated data. Technical Report UCB//CSD-02-1202, U.C. Berkeley Computer Science Division, 2002.Google Scholar
7. B. de Finetti. Theory of probability. Vol. 1-2. John Wiley & Sons Ltd., Chichester, 1990. Reprint of the 1975 translation.Google Scholar
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.Google Scholar
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.Google Scholar
10. J. Dickey. Multiple hypergeometric functions: Probabilistic interpretations and statistical uses. Journal of the American Statistical Association, 78: 628-637, 1983.Google Scholar
11. J. Dickey, J. Jiang, and J. Kadane. Bayesian methods for censored categorical data. Journal of the American Statistical Association, 82: 773-781, 1987.Google Scholar
12. A. Gelman, J. Carlin, H. Stern, and D. Rubin. Bayesian data analysis. Chapman & Hall, London, 1995.Google Scholar
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.Google Scholar
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.Google Scholar
15. D. Heckerman and M. Meila. An experimental comparison of several clustering and initialization methods. Machine Learning, 42: 9-29, 2001. Google Scholar
16. T. Hofmann. Probabilistic latent semantic indexing. Proceedings of the Twenty-Second Annual International SIGIR Conference, 1999. Google Scholar
17. F. Jelinek. Statistical Methods for Speech Recognition. MIT Press, Cambridge, MA, 1997. Google Scholar
18. T. Joachims. Making large-scale SVM learning practical. In Advances in Kernel Methods - Support Vector Learning. M.I.T. Press, 1999. Google Scholar
19. M. Jordan, editor. Learning in Graphical Models. MIT Press, Cambridge, MA, 1999. Google Scholar
20. M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul. Introduction to variational methods for graphical models. Machine Learning, 37: 183-233, 1999. Google Scholar
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.Google Scholar
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. Google Scholar
23. T. Minka. Estimating a Dirichlet distribution. Technical report, M.I.T., 2000.Google Scholar
24. T. P. Minka and J. Lafferty. Expectation-propagation for the generative aspect model. In Uncertainty in Artificial Intelligence (UAI), 2002. Google Scholar
25. C. Morris. Parametric empirical Bayes inference: Theory and applications. Journal of the American Statistical Association, 78(381): 47-65, 1983. With discussion.Google Scholar
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.Google Scholar
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. Google Scholar
28. C. Papadimitriou, H. Tamaki, P. Raghavan, and S. Vempala. Latent semantic indexing: A probabilistic analysis. pages 159-168, 1998. Google Scholar
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. Google Scholar
30. J. Rennie. Improving multi-class text classification with naive Bayes. Technical Report AITR-2001- 004, M.I.T., 2001.Google Scholar
31. G. Ronning. Maximum likelihood estimation of Dirichlet distributions. Journal of Statistcal Computation and Simulation, 34(4): 215-221, 1989.Google Scholar
32. G. Salton and M. McGill, editors. Introduction to Modern Information Retrieval. McGraw-Hill, 1983. Google Scholar

Index Terms

1. Latent dirichlet allocation

• Information
• Contributors

• Published in

  

  The Journal of Machine Learning Research  Volume 3, Issue

  3/1/2003

  1437 pages

  Publisher

  Publication History

  • Online: 1 March 2003
  • Published: 1 March 2003

  Conference

• Bibliometrics
• Citations6,079

• Article Metrics

  • View Citations

  • Downloads (Last 12 months)2,146
  • Downloads (Last 6 weeks)193

  Other Metrics