Show simple item record

dc.contributor.authorWang, Jim Jing-Yan
dc.contributor.authorAbdulJabbar, Mustafa Abdulmajeed
dc.date.accessioned2015-08-04T07:02:06Z
dc.date.available2015-08-04T07:02:06Z
dc.date.issued2012
dc.identifier.isbn9780889869219
dc.identifier.doi10.2316/P.2012.778-049
dc.identifier.urihttp://hdl.handle.net/10754/564481
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.
dc.publisherACTA Press
dc.relation.urlhttp://arxiv.org/abs/arXiv:1208.3845v3
dc.subjectData representation
dc.subjectGraph regularization
dc.subjectMultiple Kernel Learning
dc.subjectNonnegtive matrix factorization
dc.titleMultiple Kernel Learning for adaptive graph regularized nonnegative matrix factorization
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.identifier.journalSignal Processing, Pattern Recognition and Applications / 779: Computer Graphics and Imaging
dc.conference.date18 June 2012 through 20 June 2012
dc.conference.nameIASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2012
dc.conference.locationCrete
dc.identifier.arxividarXiv:1208.3845
kaust.personWang, Jim Jing-Yan
kaust.personAbdulJabbar, Mustafa Abdulmajeed


This item appears in the following Collection(s)

Show simple item record