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

Nguyen, Tien-Vu, Phung, Dinh and Venkatesh, Svetha 2013, Topic model kernel : an empirical study towards probabilistically reduced features for classification, in ICONIP 2013 : Neural information processing : 20th International Conference, Daegu, Korea, November 3-7, 2013. Proceedings, Springer, Berlin, Germany, pp. 124-131, doi: 10.1007/978-3-642-42042-9_16.

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Title Topic model kernel : an empirical study towards probabilistically reduced features for classification
Author(s) Nguyen, Tien-Vu
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Neural Information Processing. Conference (20th : 2013 : Daegu, Korea)
Conference location Daegu, Korea
Conference dates 3-7 Nov. 2013
Title of proceedings ICONIP 2013 : Neural information processing : 20th International Conference, Daegu, Korea, November 3-7, 2013. Proceedings
Editor(s) Lee, Minho
Hirose, Akira
Hou, Zeng-Guang
Kil, Rhee Man
Publication date 2013
Series Lecture Notes in Computer Science, v. 8227
Conference series Neural Information Processing Conference
Start page 124
End page 131
Total pages 8
Publisher Springer
Place of publication Berlin, Germany
Keyword(s) Classification
Hierarchical dirichlet process
Kernel method
Latent dirichlet allocation
Support vector machine
Topic modelling features
Summary 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.
ISBN 3642420427
9783642420429
Language eng
DOI 10.1007/978-3-642-42042-9_16
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2013
Copyright notice ©2013, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30061608

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
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