CTF-PSF: Coupled Tensor Factorization with Partially Shared Factors

Wu, Q; Li, X; Do, Q; Fan, J; Ge, R; Wang, J

CTF-PSF: Coupled Tensor Factorization with Partially Shared Factors

Wu, Q Li, X Do, Q Fan, J Ge, R Wang, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2018, 2018-July
Issue Date:: 2018-10-10

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Field	Value	Language
dc.contributor.author	Wu, Q	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Do, Q	en_US
dc.contributor.author	Fan, J	en_US
dc.contributor.author	Ge, R	en_US
dc.contributor.author	Wang, J	en_US
dc.date.issued	2018-10-10	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2018, 2018-July	en_US
dc.identifier.isbn	9781509060146	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133227
dc.description.abstract	© 2018 IEEE. Coupled matrix-tensor factorization has been suc- cessfully applied in various fields in the processing of coupled data. However, the unshared components between coupled data tend to make the joint decomposition inaccurate. In order to solve this problem, in this work, we propose a method to improve the traditional method by combining individual decomposition and coupled decomposition to analyze the shared and unshared components. Numerical experiments are given to illustrate the advantages of the proposed method compared to the existing approaches.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2018.8489754	en_US
dc.title	CTF-PSF: Coupled Tensor Factorization with Partially Shared Factors	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-July	en_US
utslib.for	0805 Distributed Computing	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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2018-July	en_US

Abstract:

© 2018 IEEE. Coupled matrix-tensor factorization has been suc- cessfully applied in various fields in the processing of coupled data. However, the unshared components between coupled data tend to make the joint decomposition inaccurate. In order to solve this problem, in this work, we propose a method to improve the traditional method by combining individual decomposition and coupled decomposition to analyze the shared and unshared components. Numerical experiments are given to illustrate the advantages of the proposed method compared to the existing approaches.

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http://hdl.handle.net/10453/133227