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    When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

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    Name:
    SparCod-Rank NCAA R1 170427.pdf
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    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Wang, Jim Jing-Yan
    Cui, Xuefeng
    Yu, Ge
    Guo, Lili
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2017-06-28
    Online Publication Date
    2017-06-28
    Print Publication Date
    2019-03
    Permanent link to this record
    http://hdl.handle.net/10754/625649
    
    Metadata
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    Abstract
    Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.
    Citation
    Wang JJ-Y, Cui X, Yu G, Guo L, Gao X (2017) When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores. Neural Computing and Applications. Available: http://dx.doi.org/10.1007/s00521-017-3102-9.
    Sponsors
    The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) and the National Natural Science Foundation of China under the Grant No. 61502463.
    Publisher
    Springer Nature
    Journal
    Neural Computing and Applications
    DOI
    10.1007/s00521-017-3102-9
    Additional Links
    http://link.springer.com/article/10.1007/s00521-017-3102-9
    ae974a485f413a2113503eed53cd6c53
    10.1007/s00521-017-3102-9
    Scopus Count
    Collections
    Articles; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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