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    Sparse structure regularized ranking

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    Type
    Article
    Authors
    Wang, Jim Jing-Yan
    Sun, Yijun
    Gao, Xin cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Structural and Functional Bioinformatics Group
    Date
    2014-04-17
    Online Publication Date
    2014-04-17
    Print Publication Date
    2015-01
    Permanent link to this record
    http://hdl.handle.net/10754/575597
    
    Metadata
    Show full item record
    Abstract
    Learning 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.
    Citation
    Wang, J. J.-Y., Sun, Y., & Gao, X. (2014). Sparse structure regularized ranking. Multimedia Tools and Applications, 74(2), 635–654. doi:10.1007/s11042-014-1939-9
    Sponsors
    Jim 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.
    Publisher
    Springer Nature
    Journal
    Multimedia Tools and Applications
    DOI
    10.1007/s11042-014-1939-9
    ae974a485f413a2113503eed53cd6c53
    10.1007/s11042-014-1939-9
    Scopus Count
    Collections
    Articles; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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