1999 ProbabilisticLatentSemanticAnalysis

• (Hofmann, 1999b) ⇒ Thomas Hofmann. (1999). “Probabilistic Latent Semantic Analysis.” In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI 1999).

Subject Headings: pLSA, Document Topic Modeling.

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• ~918 http://scholar.google.com/scholar?q=%22Probabilistic+Latent+Semantic+Analysis%22+1999

Quotes

Abstract

Probabilistic 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 related areas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results in a more principled approach which has a solid foundation in statistics. In order to avoid over fitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of experiments.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1999 ProbabilisticLatentSemanticAnalysis	Thomas Hofmann			Probabilistic Latent Semantic Analysis			http://www.cs.brown.edu/people/th/papers/Hofmann-UAI99.pdf