Probabilistic tensor analysis with akaike and Bayesian information criteria

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
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011001829OK.pdf308.42 kB
Adobe PDF
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.
Please use this identifier to cite or link to this item: