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2007
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Bayesian Folding-In with Dirichlet Kernels for PLSI

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Bayesian Folding-In with Dirichlet Kernels for PLSI
Probabilistic latent semantic indexing (PLSI) represents documents of a collection as mixture proportions of latent topics, which are learned from the collection by an expectation maximization (EM) algorithm. New documents or queries need to be folded into the latent topic space by a simplified version of the EM-algorithm. During PLSIFolding-in of a new document, the topic mixtures of the known documents are ignored. This may lead to a suboptimal model of the extended collection. Our new approach incorporates the topic mixtures of the known documents in a Bayesian way during foldingin. That knowledge is modeled as prior distribution over the topic simplex using a kernel density estimate of Dirichlet kernels. We demonstrate the advantages of the new Bayesian folding-in using real text data.
Alexander Hinneburg, Hans-Henning Gabriel, Andr&eg
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICDM
Authors Alexander Hinneburg, Hans-Henning Gabriel, Andrè Gohr
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