Bayesian Tensor Analysis
Publication Type
Conference Proceeding Article
Publication Date
5-2008
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.
Discipline
Computer Sciences
Publication
IEEE International Joint Conference on Neural Networks (IJCNN2008)
First Page
1402
Last Page
1409
ISBN
9781424418206
Identifier
10.1109/IJCNN.2008.4633981
Publisher
IEEE
Citation
TAO, Dacheng; SUN, Jimeng; SHEN, Jialie; WU, Xindong; LI, Xuelong; Maybank, Stephen J.; and Faloutsos, Christos.
Bayesian Tensor Analysis. (2008). IEEE International Joint Conference on Neural Networks (IJCNN2008). 1402-1409.
Available at: https://ink.library.smu.edu.sg/sis_research/411
Additional URL
http://dx.doi.org/10.1109/IJCNN.2008.4633981