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Topic model kernel : an empirical study towards probabilistically reduced features for classification

Version 2 2024-06-05, 11:47
Version 1 2014-10-28, 10:27
conference contribution
posted on 2024-06-05, 11:47 authored by TV Nguyen, D Phung, Svetha VenkateshSvetha Venkatesh
Probabilistic topic models have become a standard in modern machine learning with wide applications in organizing and summarizing ‘documents’ in high-dimensional data such as images, videos, texts, gene expression data, and so on. Representing data by dimensional reduction of mixture proportion extracted from topic models is not only richer in semantics than bag-of-word interpretation, but also more informative for classification tasks. This paper describes the Topic Model Kernel (TMK), a high dimensional mapping for Support Vector Machine classification of data generated from probabilistic topic models. The applicability of our proposed kernel is demonstrated in several classification tasks from real world datasets. We outperform existing kernels on the distributional features and give the comparative results on non-probabilistic data types.

History

Pagination

124-131

Location

Daegu, Korea

Start date

2013-11-03

End date

2013-11-07

ISBN-13

9783642420429

ISBN-10

3642420427

Language

eng

Publication classification

E1 Full written paper - refereed, E Conference publication

Copyright notice

2013, Springer

Extent

94

Editor/Contributor(s)

Lee M, Hirose A, Hou ZG, Kil R

Title of proceedings

ICONIP 2013 : Neural information processing : 20th International Conference, Daegu, Korea, November 3-7, 2013. Proceedings

Event

Neural Information Processing. Conference (20th : 2013 : Daegu, Korea)

Publisher

Springer

Place of publication

Berlin, Germany

Series

Lecture Notes in Computer Science, v. 8227