Dirichlet mixture allocation for multiclass document collections modeling

Bian, W; Tao, D

Dirichlet mixture allocation for multiclass document collections modeling

Bian, W Tao, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2009, pp. 711 - 715
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Bian, W	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2009, pp. 711 - 715	en_US
dc.identifier.isbn	9780769538952	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17280
dc.description.abstract	Topic model, Latent Dirichlet Allocation (LDA), is an effective tool for statistical analysis of large collections of documents. In LDA, each document is modeled as a mixture of topics and the topic proportions are generated from the unimodal Dirichlet distribution prior. When a collection of documents are drawn from multiple classes, this unimodal prior is insufficient for data fitting. To solve this problem, we exploit the multimodal Dirichlet mixture prior, and propose the Dirichlet mixture allocation (DMA). We report experiments on the popular TDT2 Corpus demonstrating that DMA models a collection of documents more precisely than LDA when the documents are obtained from multiple classes. © 2009 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDM.2009.102	en_US
dc.title	Dirichlet mixture allocation for multiclass document collections modeling	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Miami Beach, FL	en_US
dc.location.activity	Beijing, China
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Topic model, Latent Dirichlet Allocation (LDA), is an effective tool for statistical analysis of large collections of documents. In LDA, each document is modeled as a mixture of topics and the topic proportions are generated from the unimodal Dirichlet distribution prior. When a collection of documents are drawn from multiple classes, this unimodal prior is insufficient for data fitting. To solve this problem, we exploit the multimodal Dirichlet mixture prior, and propose the Dirichlet mixture allocation (DMA). We report experiments on the popular TDT2 Corpus demonstrating that DMA models a collection of documents more precisely than LDA when the documents are obtained from multiple classes. © 2009 IEEE.

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http://hdl.handle.net/10453/17280