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Topic model kernel : an empirical study towards probabilistically reduced features for classification
conference contribution
posted on 2013-01-01, 00:00 authored by Tien Vu Nguyen, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshProbabilistic 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
Event
Neural Information Processing. Conference (20th : 2013 : Daegu, Korea)Source
Neural information processing : 20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. ProceedingsSeries
Lecture Notes in Computer Science, v. 8227Pagination
124 - 131Publisher
SpringerLocation
Daegu, KoreaPlace of publication
Berlin, GermanyPublisher DOI
Start date
2013-11-03End date
2013-11-07ISBN-13
9783642420429ISBN-10
3642420427Language
engPublication classification
E1 Full written paper - refereed; E Conference publicationCopyright notice
2013, SpringerExtent
94Editor/Contributor(s)
M Lee, A Hirose, Z Hou, R KilTitle of proceedings
ICONIP 2013 : Neural information processing : 20th International Conference, Daegu, Korea, November 3-7, 2013. ProceedingsUsage metrics
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