Bayesian tensor analysis

Dacheng Tao, Jimeng Sun, Jialie Shen, Xindong Wu, Xuelong Li, Stephen J. Maybank, Christos Faloutsos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages1402-1409
Number of pages8
DOIs
StatePublished - Nov 24 2008
Externally publishedYes
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: Jun 1 2008Jun 8 2008

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2008 International Joint Conference on Neural Networks, IJCNN 2008
CountryChina
CityHong Kong
Period6/1/086/8/08

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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