Topic model kernel classification with probabilistically reduced features

Nguyen, Vu, Phung, Dinh and Venkatesh, Svetha 2015, Topic model kernel classification with probabilistically reduced features, Journal of data science, vol. 13, no. 2, pp. 323-340.

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Title Topic model kernel classification with probabilistically reduced features
Author(s) Nguyen, Vu
Phung, DinhORCID iD for Phung, Dinh
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Journal name Journal of data science
Volume number 13
Issue number 2
Start page 323
End page 340
Total pages 18
Publisher Department of Statistics, Columbia University
Place of publication New York, N.Y.
Publication date 2015-04
ISSN 1680-743X
Keyword(s) topic models
Bayesian nonparametic
support vector machine
kernel method
dimensionality reduction
Summary Probabilistic topic models have become a standard in modern machine learning to deal with a wide range of applications. Representing data by dimensional reduction of mixture proportion extracted from topic models is not only richer in semantics interpretation, but could also be informative for classification tasks. In this paper, we describe the Topic Model Kernel (TMK), a topicbased kernel for Support Vector Machine classification on data being processed by probabilistic topic models. The applicability of our proposed kernel is demonstrated in several classification tasks with real world datasets. TMK outperforms existing kernels on the distributional features and give comparative results on nonprobabilistic data types.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Department of Statistics, Columbia University
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