Probabilistic Latent Semantic Analysis

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 overfitting, 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.

Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)

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• Visualizing Topics with Multi-Word Expressions
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• Mixed membership analysis of high-throughput interaction studies: Relational data
• A Tutorial on Probabilistic Latent Semantic Analysis
• Using Access Data for Paper Recommendations on ArXiv.org
• Factorized Topic Models
• Message-Passing Inference on a Factor Graph for Collaborative Filtering
• Approximate Maximum A Posteriori Inference with Entropic Priors
• Comparison Latent Semantic and WordNet Approach for Semantic Similarity Calculation
• The Latent Bernoulli-Gauss Model for Data Analysis