Topic model kernel classification with probabilistically reduced features

Vu Nguyen, Dinh Phung, Svetha Venkatesh

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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 topic based 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.
Original languageEnglish
Pages (from-to)323-340
Number of pages18
JournalJournal of Data Science
Volume13
Issue number2
Publication statusPublished - Apr 2015
Externally publishedYes

Keywords

  • Topic Models
  • Bayesian Nonparametric
  • Support Vector Machine
  • Kernel Method
  • Classification
  • Dimensionality Reduction

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