Bayesian Tensor Analysis
Conference Proceeding Article
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.
Data Management and Analytics
IEEE International Joint Conference on Neural Networks (IJCNN2008)
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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/411