On an equivalence between PLSI and LDA

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On an equivalence between PLSI and LDA

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Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval table of contents

Toronto, Canada

POSTER SESSION: Posters table of contents

Pages: 433 - 434

Year of Publication: 2003

ISBN:1-58113-646-3

Authors

Mark Girolami	University of Paisley, UK
Ata Kabán	University of Birmingham, UK

Sponsor

ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 17, Downloads (12 Months): 110, Citation Count: 6

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abstract references cited by index terms collaborative colleagues

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ABSTRACT

Latent Dirichlet Allocation (LDA) is a fully generative approach to language modelling which overcomes the inconsistent generative semantics of Probabilistic Latent Semantic Indexing (PLSI). This paper shows that PLSI is a maximum a posteriori estimated LDA model under a uniform Dirichlet prior, therefore the perceived shortcomings of PLSI can be resolved and elucidated within the LDA framework.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	David M. Blei , Andrew Y. Ng , Michael I. Jordan, Latent dirichlet allocation, The Journal of Machine Learning Research, 3, p.993-1022, 3/1/2003 [doi>10.1162/jmlr.2003.3.4-5.993]
	2	Thomas Hofmann, Probabilistic latent semantic indexing, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.50-57, August 15-19, 1999, Berkeley, California, United States [doi>10.1145/312624.312649]
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CITED BY 6

	Leif Azzopardi , Mark Girolami , Keith van Risjbergen, Investigating the relationship between language model perplexity and IR precision-recall measures, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada
	Peter V. Gehler , Alex D. Holub , Max Welling, The rate adapting poisson model for information retrieval and object recognition, Proceedings of the 23rd international conference on Machine learning, p.337-344, June 25-29, 2006, Pittsburgh, Pennsylvania
	Xing Wei , W. Bruce Croft, LDA-based document models for ad-hoc retrieval, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA
	Hu Wu , Yongji Wang , Xiang Cheng, Incremental probabilistic latent semantic analysis for automatic question recommendation, Proceedings of the 2008 ACM conference on Recommender systems, October 23-25, 2008, Lausanne, Switzerland
	Laurence A. Park , Kotagiri Ramamohanarao, Efficient storage and retrieval of probabilistic latent semantic information for information retrieval, The VLDB Journal — The International Journal on Very Large Data Bases, v.18 n.1, p.141-155, January 2009
	Xiaohui Wu , Jun Yan , Ning Liu , Shuicheng Yan , Ying Chen , Zheng Chen, Probabilistic latent semantic user segmentation for behavioral targeted advertising, Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising, p.10-17, June 28-28, 2009, Paris, France