Graph regularized multilayer concept factorization for data representation

Li, X; Shen, X; Shu, Z; Ye, Q; Zhao, C

Graph regularized multilayer concept factorization for data representation

Li, X Shen, X

Shu, Z Ye, Q Zhao, C

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2017, 238 pp. 139 - 151
Issue Date:: 2017-05-17

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Field	Value	Language
dc.contributor.author	Li, X	en_US
dc.contributor.author	Shen, X https://orcid.org/0000-0001-8494-4532	en_US
dc.contributor.author	Shu, Z	en_US
dc.contributor.author	Ye, Q	en_US
dc.contributor.author	Zhao, C	en_US
dc.date.issued	2017-05-17	en_US
dc.identifier.citation	Neurocomputing, 2017, 238 pp. 139 - 151	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126875
dc.description.abstract	© 2017 Elsevier B.V. Previous studies have demonstrated that matrix factorization techniques, such as Nonnegative Matrix Factorization (NMF) and Concept Factorization (CF), have yielded impressive results in image processing and data representation. However, conventional CF and its variants with single layer factorization fail to capture the intrinsic structure of data. In this paper, we propose a novel sequential factorization method, namely Graph regularized Multilayer Concept Factorization (GMCF) for clustering. GMCF is a multi-stage procedure, which decomposes the observation matrix iteratively in a number of layers. In addition, GMCF further incorporates graph Laplacian regularization in each layer to efficiently preserve the manifold structure of data. An efficient iterative updating scheme is developed for optimizing GMCF. The convergence of this algorithm is strictly proved; the computational complexity is detailedly analyzed. Extensive experiments demonstrate that GMCF owns the superiorities in terms of data representation and clustering performance.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2017.01.045	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Graph regularized multilayer concept factorization for data representation	en_US
dc.type	Journal Article
utslib.citation.volume	238	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	238	en_US

Abstract:

© 2017 Elsevier B.V. Previous studies have demonstrated that matrix factorization techniques, such as Nonnegative Matrix Factorization (NMF) and Concept Factorization (CF), have yielded impressive results in image processing and data representation. However, conventional CF and its variants with single layer factorization fail to capture the intrinsic structure of data. In this paper, we propose a novel sequential factorization method, namely Graph regularized Multilayer Concept Factorization (GMCF) for clustering. GMCF is a multi-stage procedure, which decomposes the observation matrix iteratively in a number of layers. In addition, GMCF further incorporates graph Laplacian regularization in each layer to efficiently preserve the manifold structure of data. An efficient iterative updating scheme is developed for optimizing GMCF. The convergence of this algorithm is strictly proved; the computational complexity is detailedly analyzed. Extensive experiments demonstrate that GMCF owns the superiorities in terms of data representation and clustering performance.

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http://hdl.handle.net/10453/126875