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    Bayesian tensor analysis

    Tao, D. and Sun, J. and Shen, J. and Wu, X. and Li, Xuelong 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.

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    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: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Administrator
    Date Deposited: 05 Nov 2012 11:00
    Last Modified: 09 Aug 2023 12:32


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