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dc.contributor.authorWang, Jim Jing-Yan
dc.contributor.authorSun, Yijun
dc.contributor.authorGao, Xin
dc.date.accessioned2015-08-24T08:33:46Z
dc.date.available2015-08-24T08:33:46Z
dc.date.issued2014-04-17
dc.identifier.issn13807501
dc.identifier.doi10.1007/s11042-014-1939-9
dc.identifier.urihttp://hdl.handle.net/10754/575597
dc.description.abstractLearning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.
dc.description.sponsorshipJim Jing-Yan Wang and Yijun Sun are in part supported by US National Science Foundation under grant No. DBI-1062362. The study is supported by grants from Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, China, and King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
dc.publisherSpringer Nature
dc.subjectMultimedia database retrieval
dc.subjectRanking score
dc.subjectSparse representation
dc.titleSparse structure regularized ranking
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalMultimedia Tools and Applications
dc.contributor.institutionSUNY Buffalo, New York State Ctr Excellence Bioinformat & Life, Buffalo, NY 14203 USA
dc.contributor.institutionSoochow Univ, Prov Key Lab Comp Informat Proc Technol, Suzhou 215006, Peoples R China
dc.contributor.institutionSUNY Buffalo, Dept Biostat, Dept Comp Sci & Engn, Dept Microbiol & Immunol, Buffalo, NY 14203 USA
kaust.personGao, Xin


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