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  Kernel Topic Models

Hennig, P., Stern, D., Herbich, R., & Graepel, T. (2012). Kernel Topic Models. In JMLR Workshop and Conference Proceedings (pp. 511-511). Cambridge, MA, USA: JMLR.

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 Creators:
Hennig, P1, Author           
Stern, D, Author
Herbich, R, Author
Graepel, T, Author
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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Free keywords: Abt. Schölkopf
 Abstract: {Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions. This allows documents to be associated with elements of a Hilbert space, admitting kernel topic models (KTM), modelling temporal, spatial, hierarchical, social and other structure between documents. The main challenge is efficient approximate inference on the latent Gaussian. We present an approximate algorithm cast around a Laplace approximation in a transformed basis. The KTM can also be interpreted as a type of Gaussian process latent variable model, or as a topic model conditional on document features, uncovering links between earlier work in these areas.}

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 Dates: 2012-04
 Publication Status: Issued
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 Identifiers: BibTex Citekey: HennigSHG2012
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Title: Fifteenth International Conference on Artificial Intelligence and Statistics (AI Statistics 2012)
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Title: JMLR Workshop and Conference Proceedings
Source Genre: Proceedings
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Publ. Info: Cambridge, MA, USA : JMLR
Pages: - Volume / Issue: 22 Sequence Number: - Start / End Page: 511 - 511 Identifier: -