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: UNSPECIFIED (ed.) International Joint Conference on Neural Networks. New York, USA: Institute of Electrical and Electronics Engineers, pp. 1402-1409. ISBN 9781424418206.
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.
Metadata
| Item Type: | Book Section |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 12 Jul 2013 13:31 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7677 |
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