Multiple Kernel Learning for adaptive graph regularized nonnegative matrix factorization

Handle URI:
http://hdl.handle.net/10754/564481
Title:
Multiple Kernel Learning for adaptive graph regularized nonnegative matrix factorization
Authors:
Wang, Jim Jing-Yan; AbdulJabbar, Mustafa Abdulmajeed
Abstract:
Nonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and linear representation of non-negative data. Recently, Graph regularized NMF (GrNMF) is proposed to find a compact representation, which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In GNMF, an affinity graph is constructed from the original data space to encode the geometrical information. In this paper, we propose a novel idea which engages a Multiple Kernel Learning approach into refining the graph structure that reflects the factorization of the matrix and the new data space. The GrNMF is improved by utilizing the graph refined by the kernel learning, and then a novel kernel learning method is introduced under the GrNMF framework. Our approach shows encouraging results of the proposed algorithm in comparison to the state-of-the-art clustering algorithms like NMF, GrNMF, SVD etc.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Computational Bioscience Research Center (CBRC)
Publisher:
ACTA Press
Journal:
Signal Processing, Pattern Recognition and Applications / 779: Computer Graphics and Imaging
Conference/Event name:
IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2012
Issue Date:
2012
DOI:
10.2316/P.2012.778-049
ARXIV:
arXiv:1208.3845
Type:
Conference Paper
ISBN:
9780889869219
Additional Links:
http://arxiv.org/abs/arXiv:1208.3845v3
Appears in Collections:
Conference Papers; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Jim Jing-Yanen
dc.contributor.authorAbdulJabbar, Mustafa Abdulmajeeden
dc.date.accessioned2015-08-04T07:02:06Zen
dc.date.available2015-08-04T07:02:06Zen
dc.date.issued2012en
dc.identifier.isbn9780889869219en
dc.identifier.doi10.2316/P.2012.778-049en
dc.identifier.urihttp://hdl.handle.net/10754/564481en
dc.description.abstractNonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and linear representation of non-negative data. Recently, Graph regularized NMF (GrNMF) is proposed to find a compact representation, which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In GNMF, an affinity graph is constructed from the original data space to encode the geometrical information. In this paper, we propose a novel idea which engages a Multiple Kernel Learning approach into refining the graph structure that reflects the factorization of the matrix and the new data space. The GrNMF is improved by utilizing the graph refined by the kernel learning, and then a novel kernel learning method is introduced under the GrNMF framework. Our approach shows encouraging results of the proposed algorithm in comparison to the state-of-the-art clustering algorithms like NMF, GrNMF, SVD etc.en
dc.publisherACTA Pressen
dc.relation.urlhttp://arxiv.org/abs/arXiv:1208.3845v3en
dc.subjectData representationen
dc.subjectGraph regularizationen
dc.subjectMultiple Kernel Learningen
dc.subjectNonnegtive matrix factorizationen
dc.titleMultiple Kernel Learning for adaptive graph regularized nonnegative matrix factorizationen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalSignal Processing, Pattern Recognition and Applications / 779: Computer Graphics and Imagingen
dc.conference.date18 June 2012 through 20 June 2012en
dc.conference.nameIASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2012en
dc.conference.locationCreteen
dc.identifier.arxividarXiv:1208.3845en
kaust.authorWang, Jim Jing-Yanen
kaust.authorAbdulJabbar, Mustafa Abdulmajeeden
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