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  • Local and global regularized sparse coding for data representation

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    Shu1PUB2759.pdf (926.8Kb)
    Author(s)
    Shu, Zhenqiu
    Zhou, Jun
    Huang, Pu
    Yu, Xun
    Yang, Zhangjing
    Zhao, Chunxia
    Griffith University Author(s)
    Zhou, Jun
    Yu, Alex
    Year published
    2016
    Metadata
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    Abstract
    Recently, sparse coding has been widely adopted for data representation in real-world applications. In order to consider the geometric structure of data, we propose a novel method, local and global regularized sparse coding (LGSC), for data representation. LGSC not only models the global geometric structure by a global regression regularizer, but also takes into account the manifold structure using a local regression regularizer. Compared with traditional sparse coding methods, the proposed method can preserve both global and local geometric structures of the original high-dimensional data in a new representation space. ...
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    Recently, sparse coding has been widely adopted for data representation in real-world applications. In order to consider the geometric structure of data, we propose a novel method, local and global regularized sparse coding (LGSC), for data representation. LGSC not only models the global geometric structure by a global regression regularizer, but also takes into account the manifold structure using a local regression regularizer. Compared with traditional sparse coding methods, the proposed method can preserve both global and local geometric structures of the original high-dimensional data in a new representation space. Experimental results on benchmark datasets show that the proposed method can improve the performance of clustering.
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    Journal Title
    Neurocomputing
    Volume
    175
    Issue
    Part A
    DOI
    https://doi.org/10.1016/j.neucom.2015.10.048
    Copyright Statement
    © 2016 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Computer Vision
    Information and Computing Sciences
    Engineering
    Psychology and Cognitive Sciences
    Publication URI
    http://hdl.handle.net/10072/100911
    Collection
    • Journal articles

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