Multiple Kernel Learning for adaptive graph regularized nonnegative matrix factorization

Jim Jing-Yan Wang, Mustafa Abdulmajeed AbdulJabbar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


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.
Original languageEnglish (US)
Title of host publicationSignal Processing, Pattern Recognition and Applications / 779: Computer Graphics and Imaging
PublisherACTA Press
Number of pages8
ISBN (Print)9780889869219
StatePublished - 2012

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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