Difference between revisions of "Reisinger et al 2010: Spherical Topic Models"
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== Summary == | == Summary == | ||
| − | This is | + | This is a recent [[Category::paper]] that presents [[UsesMethod::Spherical Mixture Model]] and [[UsesMethod::Variational Inference]] methods for [[UsesMethod::Latent Dirichlet Allocation]], which is an alternative training method for general problems in [[AddressesProblem::Topic model]]. |
== Brief Description of the method == | == Brief Description of the method == | ||
Revision as of 17:13, 21 November 2011
Contents
Citation
Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond J. Mooney, "Spherical Topic Models", in Proceedings of the 27th International Conference on Machine Learning (ICML 2010), 2010.
Online version
Summary
This is a recent paper that presents Spherical Mixture Model and Variational Inference methods for Latent Dirichlet Allocation, which is an alternative training method for general problems in Topic model.
Brief Description of the method
This paper first formulates the problem as xxx, then discusses the use of and the use of xxx to. In this section, we will first summarize the method they use
Dataset and Experiment Settings
[[File:]]
Experimental Results
The authors performed three major experiments. The first experiment is the . The second experiment explores.
Exp
[[File:]]
Exp
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Exp
[[File:]]
Related Papers
This paper is related to many papers in three dimensions.
(1) .
(2) .
(3) .
