Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria
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...
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sg-smu-ink.sis_research-13982010-09-24T06:36:22Z Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria TAO, Dacheng SUN, Jimeng SHEN, Jialie Wu, Xindong LI, Xuelong Maybank, Stephen J. Faloutsos, Christos 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. 2007-09-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/399 info:doi/10.1007/978-3-540-69158-7_82 http://dx.doi.org/10.1007/978-3-540-69158-7_82 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing |
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Databases and Information Systems Numerical Analysis and Scientific Computing TAO, Dacheng SUN, Jimeng SHEN, Jialie Wu, Xindong LI, Xuelong Maybank, Stephen J. Faloutsos, Christos Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria |
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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. |
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TAO, Dacheng SUN, Jimeng SHEN, Jialie Wu, Xindong LI, Xuelong Maybank, Stephen J. Faloutsos, Christos |
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TAO, Dacheng SUN, Jimeng SHEN, Jialie Wu, Xindong LI, Xuelong Maybank, Stephen J. Faloutsos, Christos |
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TAO, Dacheng |
title |
Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria |
title_short |
Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria |
title_full |
Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria |
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Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria |
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Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria |
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probabilistic tensor analysis with akaike and bayesian information criteria |
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Institutional Knowledge at Singapore Management University |
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2007 |
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https://ink.library.smu.edu.sg/sis_research/399 http://dx.doi.org/10.1007/978-3-540-69158-7_82 |
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