Probabilistic Latent Semantic Analysis

Understand

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.