Probabilistic tensor analysis with akaike and Bayesian information criteria

Tao, D; Sun, J; Wu, X; Li, X; Shen, J; Maybank, SJ; Faloutsos, C

Probabilistic tensor analysis with akaike and Bayesian information criteria

Tao, D

Sun, J Wu, X Li, X Shen, J Maybank, SJ Faloutsos, C

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, 4984 LNCS (PART 1), pp. 791 - 801
Issue Date:: 2008-10-27

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Field	Value	Language
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Sun, J	en_US
dc.contributor.author	Wu, X	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Shen, J	en_US
dc.contributor.author	Maybank, SJ	en_US
dc.contributor.author	Faloutsos, C	en_US
dc.date.issued	2008-10-27	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, 4984 LNCS (PART 1), pp. 791 - 801	en_US
dc.identifier.isbn	3540691545	en_US
dc.identifier.isbn	9783540691549	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17621
dc.description.abstract	From data mining to computer vision, from visual surveillance to biometrics research, from biomedical imaging to bioinformatics, and from multimedia retrieval to information management, a large amount of data are naturally represented by multidimensional arrays, i.e., tensors. However, conventional probabilistic graphical models with probabilistic inference only model data in vector format, although they are very important in many statistical problems, e.g., model selection. Is it possible to construct multilinear probabilistic graphical models for tensor format data to conduct probabilistic inference, e.g., model selection? This paper provides a positive answer based on the proposed decoupled probabilistic model by developing the probabilistic tensor analysis (PTA), which selects suitable model for tensor format data modeling based on Akaike information criterion (AIC) and Bayesian information criterion (BIC). Empirical studies demonstrate that PTA associated with AIC and BIC selects correct number of models. © 2008 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-540-69158-7_82	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Probabilistic tensor analysis with akaike and Bayesian information criteria	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 1	en_US
utslib.citation.volume	4984 LNCS	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Kitakyushu, Japan	en_US
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
utslib.copyright.status	closed_access
pubs.issue	PART 1	en_US
pubs.publication-status	Published	en_US
pubs.volume	4984 LNCS	en_US

Abstract:

From data mining to computer vision, from visual surveillance to biometrics research, from biomedical imaging to bioinformatics, and from multimedia retrieval to information management, a large amount of data are naturally represented by multidimensional arrays, i.e., tensors. However, conventional probabilistic graphical models with probabilistic inference only model data in vector format, although they are very important in many statistical problems, e.g., model selection. Is it possible to construct multilinear probabilistic graphical models for tensor format data to conduct probabilistic inference, e.g., model selection? This paper provides a positive answer based on the proposed decoupled probabilistic model by developing the probabilistic tensor analysis (PTA), which selects suitable model for tensor format data modeling based on Akaike information criterion (AIC) and Bayesian information criterion (BIC). Empirical studies demonstrate that PTA associated with AIC and BIC selects correct number of models. © 2008 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/17621