Supervised nonnegative tensor factorization with Maximum-Margin Constraint

Wu, F; Tan, X; Yang, Y; Tao, D; Tang, S; Zhuang, Y

Supervised nonnegative tensor factorization with Maximum-Margin Constraint

Wu, F Tan, X Yang, Y

Tao, D

Tang, S Zhuang, Y

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013, 2013, pp. 962 - 968
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Wu, F	en_US
dc.contributor.author	Tan, X	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Tang, S	en_US
dc.contributor.author	Zhuang, Y	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013, 2013, pp. 962 - 968	en_US
dc.identifier.isbn	9781577356158	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28342
dc.description.abstract	Non-negative tensor factorization (NTF) has attracted great attention in the machine learning community. In this paper, we extend traditional non-negative tensor factorization into a supervised discriminative decomposition, referred as Supervised Non-negative Tensor Factorization with Maximum-Margin Constraint (SNTFM2). SNTFM2 formulates the optimal discriminative factorization of non-negative tensorial data as a coupled least-squares optimization problem via a maximum-margin method. As a result, SNTFM2 not only faithfully approximates the tensorial data by additive combinations of the basis, but also obtains a strong generalization power to discriminative analysis (in particular for classification in this paper). The experimental results show the superiority of our proposed model over state-of-the-art techniques on both toy and real world data sets. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.	en_US
dc.relation.ispartof	Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013	en_US
dc.title	Supervised nonnegative tensor factorization with Maximum-Margin Constraint	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Bellevue, Washington, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Non-negative tensor factorization (NTF) has attracted great attention in the machine learning community. In this paper, we extend traditional non-negative tensor factorization into a supervised discriminative decomposition, referred as Supervised Non-negative Tensor Factorization with Maximum-Margin Constraint (SNTFM2). SNTFM2 formulates the optimal discriminative factorization of non-negative tensorial data as a coupled least-squares optimization problem via a maximum-margin method. As a result, SNTFM2 not only faithfully approximates the tensorial data by additive combinations of the basis, but also obtains a strong generalization power to discriminative analysis (in particular for classification in this paper). The experimental results show the superiority of our proposed model over state-of-the-art techniques on both toy and real world data sets. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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http://hdl.handle.net/10453/28342