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dc.contributor.authorWang, Jim Jing-Yan
dc.contributor.authorBensmail, H.
dc.contributor.authorYao, N.
dc.contributor.authorGao, Xin
dc.date.accessioned2014-11-11T14:29:09Z
dc.date.available2014-11-11T14:29:09Z
dc.date.issued2013-09-26
dc.identifier.citationWang JJ-Y, Bensmail H, Yao N, Gao X (2013) Discriminative sparse coding on multi-manifolds. Knowledge-Based Systems 54: 199-206. doi:10.1016/j.knosys.2013.09.004.
dc.identifier.issn09507051
dc.identifier.doi10.1016/j.knosys.2013.09.004
dc.identifier.urihttp://hdl.handle.net/10754/334545
dc.description.abstractSparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach. 2013 The Authors. All rights reserved.
dc.language.isoen
dc.publisherElsevier BV
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/3.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/
dc.subjectData representation
dc.subjectLarge margins
dc.subjectMulti-manifolds
dc.subjectSparse coding
dc.subjectBioinformatics
dc.subjectMedical imaging
dc.subjectUltrasonic imaging
dc.subjectBreast tumor classifications
dc.subjectClass information
dc.subjectClassification approach
dc.subjectData representations
dc.subjectMatching problems
dc.subjectCodes (symbols)
dc.titleDiscriminative sparse coding on multi-manifolds
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalKnowledge-Based Systems
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionQatar Computing Research Institute, Doha 5825, Qatar
dc.contributor.institutionDepartment of Instrumentation Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
dc.contributor.institutionNational Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personWang, Jim Jing-Yan
kaust.personGao, Xin
refterms.dateFOA2018-06-13T15:49:28Z
dc.date.published-online2013-09-26
dc.date.published-print2013-12


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