Title

Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria

Publication Type

Conference Proceeding Article

Publication Date

9-2007

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.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, 13-16 November: Revised Selected Papers, Part I

Volume

4984

First Page

791

Last Page

801

ISBN

9783540691587

Identifier

10.1007/978-3-540-69158-7_82

Publisher

Springer Verlag

City or Country

Kitakyushu, Japan

Additional URL

http://dx.doi.org/10.1007/978-3-540-69158-7_82