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dc.contributor.authorWang, Jim Jing-Yan
dc.contributor.authorBensmail, Halima
dc.contributor.authorGao, Xin
dc.date.accessioned2015-08-24T08:36:17Z
dc.date.available2015-08-24T08:36:17Z
dc.date.issued2014-03
dc.identifier.citationWang, J. J.-Y., Bensmail, H., & Gao, X. (2014). Feature selection and multi-kernel learning for sparse representation on a manifold. Neural Networks, 51, 9–16. doi:10.1016/j.neunet.2013.11.009
dc.identifier.issn08936080
dc.identifier.pmid24333479
dc.identifier.doi10.1016/j.neunet.2013.11.009
dc.identifier.urihttp://hdl.handle.net/10754/575708
dc.description.abstractSparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. © 2013 Elsevier Ltd.
dc.description.sponsorshipThe study was supported by grants from Chongqing Key Laboratory of Computational Intelligence, China (Grant No. CQ-LCI-2013-02), Tianjin Key Laboratory of Cognitive Computing and Application, China, 2011 Qatar Annual Research Forum Award (Grant no. ARF2011), and King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
dc.publisherElsevier BV
dc.subjectData representation
dc.subjectFeature selection
dc.subjectManifold
dc.subjectMultiple kernel learning
dc.subjectSparse coding
dc.titleFeature selection and multi-kernel learning for sparse representation on a manifold
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalNeural Networks
dc.contributor.institutionChongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
dc.contributor.institutionQatar Computing Research Institute, Doha 5825, Qatar
kaust.personWang, Jim Jing-Yan
kaust.personGao, Xin


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