Probabilistic Latent Semantic Analysis

Probabilistic Latent Semantic Analysis. Hofmann, T.

Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of twoomode and co-occurrence data, which has applications in information retrieval and [ltering, natural language processing, ma-chine learning from text, and in related ar-eas. 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 overrtting, we propose a widely applicable generalization of maximum likelihood model by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of ex-periments.

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 title = {Probabilistic Latent Semantic Analysis},
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 abstract = {Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of twoomode and co-occurrence data, which has applications in information retrieval and [ltering, natural language processing, ma-chine learning from text, and in related ar-eas. 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 overrtting, we propose a widely applicable generalization of maximum likelihood model by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of ex-periments.},
 bibtype = {article},
 author = {Hofmann, Thomas}
}

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