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  Kernel Topic Models

Hennig, P., Stern, D., & Herbrich, R. (2012). Kernel Topic Models. In N. Lawrence, & M. Girolami (Eds.), Artificial Intelligence and Statistics, 21-23 April 2012, La Palma, Canary Islands (pp. 511-519). Madison, WI, USA: International Machine Learning Society.

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

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 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: Published in print
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 Identifiers: BibTex Citekey: HennigSHG2012
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Title: Fifteenth International Conference on Artificial Intelligence and Statistics (AI Statistics 2012)
Place of Event: La Palma, Spain
Start-/End Date: 2012-04-21 - 2012-04-23

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Title: Artificial Intelligence and Statistics, 21-23 April 2012, La Palma, Canary Islands
Source Genre: Proceedings
 Creator(s):
Lawrence, ND, Editor
Girolami, M, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 511 - 519 Identifier: -

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Title: JMLR Workshop and Conference Proceedings
Source Genre: Series
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Pages: - Volume / Issue: 22 Sequence Number: - Start / End Page: - Identifier: -