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Bayesian tensor analysis

Tao, D. and Sun, J. and Shen, J. and Wu, X. and Li, X. and Maybank, Stephen J. and Faloutsos, C. (2008) Bayesian tensor analysis. In: IEEE International Joint Conference on Neural Networks, 2008: IJCNN 2008: (IEEE World Congress on Computational Intelligence), 1-6 June 2008, Hong Kong, China.

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/IJCNN.2008.4633981

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.

Item Type: Conference or Workshop Item (Paper)
Keyword(s) / Subject(s): Bayesian methods , Computer science , Computer vision , Data mining, Graphical models, Mathematical model, Multidimensional systems, Principal component analysis, Sun, Tensile stress
School or Research Centre: Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems
Depositing User: Administrator
Date Deposited: 05 Nov 2012 11:00
Last Modified: 17 Apr 2013 12:26
URI: http://eprints.bbk.ac.uk/id/eprint/5559

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