Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14103
Title: Bayesian tensor analysis
Authors: Tao, D
Sun, J
Shen, J
Wu, X
Li, X
Maybank, S
Christos, F
Keywords: Bayes methods
Computer vision
Data mining
Tensors
Issue Date: 2008
Publisher: IEEE
Source: IEEE International Joint Conference on Neural Networks, 2008 : IJCNN 2008 : (IEEE World Congress on Computational Intelligence), 1-8 June 2008, Hong Kong, p. 1402-1409 How to cite?
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.
URI: http://hdl.handle.net/10397/14103
ISBN: 978-1-4244-1820-6
978-1-4244-1821-3 (E-ISBN)
ISSN: 1098-7576
DOI: 10.1109/IJCNN.2008.4633981
Appears in Collections:Conference Paper

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