Title

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

Research Areas

Data Management and Analytics

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

Additional URL

http://dx.doi.org/10.1109/IJCNN.2008.4633981