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Abstract: In this paper, we propose a general Bayesian treatment of the latent semantic analysis (LSA) problem. Our approach is based on multiple-mode mixture modelling of variables with categorical, discrete and continuous attributes. We demonstrate it on documents with attributes such as indicator variables, text and images. This approach can, however, be easily extended to other domains, including data mining, latent semantic indexing (LSI) and genomics. We present three Bayesian strategies for... (Update)
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BibTeX entry: (Update)
@misc{ freitas-bayesian,
author = "Nando de Freitas and Kobus Barnard",
title = "Bayesian Latent Semantic Analysis",
url = "citeseer.ist.psu.edu/article/defreitas00bayesian.html" }
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