Thomas Hofmann
- 1.DEERWESTER, S., DUMAIS, S. T., FURNAS, G. W., LANDAUER, T. K., AND HARSHMAN, R. Indexing by latent semantic analysis. Journal of the American Society for Information Science (1990).Google Scholar
- 2.DEMPSTER, A., LAIRD, N., AND RUBIN, D. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. B 39 (1977), 1-38.Google ScholarCross Ref
- 3.DUMAIS, S. T. Latent semantic indexing (lsi): Trec-3 report. In Proceedings of the Text REtrieval Conference (TREC-3) (1995), D. Harman, Ed., pp. 219-30.Google Scholar
- 4.GILDEA, D., AND HOFMANN, T. Topic-based language models using em. In Proceedings of the 6th European Conference on Speech Communication and Technology(EUROSPEECIt) (1999).Google Scholar
- 5.H OFMANN, W. Latent class models for collaborative filtering. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI) (1999). Google ScholarDigital Library
- 6.HOFMANN, T. Probabilistic latent semantic analysis. In Proceedings of the 15th Conference on Uncertainty in AI (1999). Google ScholarDigital Library
- 7.HOFMANN, T., PUZICHA, J., AND JORDAN, M. I. Unsupervised learning from dyadic data. In Advances in Neural Information Processing Systems (1999), vol. 11. Google ScholarDigital Library
- 8.LINGUISTIC DATA CONSORTIUM. TDT pilot study corpus. Catalog no. LDC98T25, 1998.Google Scholar
- 9.MCLACHLAN, G., AND BASFORD, K. E. Mixture Models. Marcel Dekker, INC, New York Basel, 1988.Google Scholar
- 10.MuaaaY, M. K., AND RICE, J. W. Differential geometry and statistics. No. 48 in Monographs on statistics and applied probability. Chapman & Hal, 1993.Google Scholar
- 11.MEAL, R., AND HINTON, G. A view of the EM algorithm that justifies incremental and other variants. In Learning in Graphical Models, M. Jordan, Ed. Kluwer Academic Publishers, 1998, pp. 355-368. Google ScholarCross Ref
- 12.PEREIRA, F., TISHBY, N., AND LEE, L. Distributional clustering of english words. In Proceedings of the A CL (1993), pp. 183-190. Google ScholarDigital Library
- 13.ROSE, K., GUREWlTZ, E., AND Fox, G. A deterministic annealing approach to clustering. Pattern Recognition Letters 11, 11 (1990), 589-594. Google ScholarDigital Library
- 14.SALTON, G., AND MCGILL, M. J. Introduction to Modern Information Retrieval. McGraw-Hill, 1983. Google ScholarDigital Library
- 15.SAUL, g., AND PEREIRA, F. Aggregate and mixedorder Markov models for statistical language processing. In Proceedings of the 2nd International Conference on Empirical Methods in Natural Language Processing (1997).Google Scholar
- 16.VOGT, C. C., AND COTTRELL, G. W. Predicting the performance of linearly combined IR systems. In Proceedings of the 21st A CM-SIGIR International Conference on Research and Development in Information Retrieval, Melbourne, Australia (1998). Google ScholarDigital Library
Index Terms
- Probabilistic latent semantic indexing
Recommendations
Latent-Space Variational Bayes
Variational Bayesian Expectation-Maximization (VBEM), an approximate inference method for probabilistic models based on factorizing over latent variables and model parameters, has been a standard technique for practical Bayesian inference. In this paper,...
Probabilistic latent maximal marginal relevance
SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrievalDiversity has been heavily motivated in the information retrieval literature as an objective criterion for result sets in search and recommender systems. Perhaps one of the most well-known and most used algorithms for result set diversification is that ...
Probabilistic latent semantic analysis
UAI'99: Proceedings of the Fifteenth conference on Uncertainty in artificial intelligenceProbabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in ...
Comments