Bayesian Tensor Analysis

Bayesian Tensor Analysis

Vector data are normally used for probabilistic graphical models with Bayesian inference. However, tensor data, i.e., multidimensional arrays, are actually natural representations of a large amount of real data, in data mining, computer vision, and many other applications. Aiming at breaking the hug...

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Bibliographic Details
Main Authors: TAO, Dacheng, SUN, Jimeng, SHEN, Jialie, WU, Xindong, LI, Xuelong, Maybank, Stephen J., Faloutsos, Christos
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2008
Subjects:
Computer Sciences
Online Access:https://ink.library.smu.edu.sg/sis_research/411
http://dx.doi.org/10.1109/IJCNN.2008.4633981
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Institution: Singapore Management University
Language: English
Description
Summary:Vector data are normally used for probabilistic graphical models with Bayesian inference. However, tensor data, i.e., multidimensional arrays, are actually natural representations of a large amount of real data, in data mining, computer vision, and many other applications. Aiming at breaking the huge gap between vectors and tensors in conventional statistical tasks, e.g., automatic model selection, this paper proposes a decoupled probabilistic algorithm, named Bayesian tensor analysis (BTA). BTA automatically selects a suitable model for tensor data, as demonstrated by empirical studies.

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