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  • Collaborative text categorization via exploiting sparse coefficients

    Author(s)
    Yao, Lina
    Sheng, Quan Z
    Wang, Xianzhi
    Wang, Shengrui
    Li, Xue
    Wang, Sen
    Griffith University Author(s)
    Wang, Sen
    Year published
    2018
    Metadata
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    Abstract
    Text categorization is widely characterized as a multi-label classification problem. Robust modeling of the semantic similarity between a query text and training texts is essential to construct an effective and accurate classifier. In this paper, we systematically investigate the Web page/text classification problem via integrating sparse representation with random measurements. In particular, we first adopt a very sparse data-independent random measurement matrix to map the original high dimensional text feature space to a lower dimensional space without loss of key information. We then propose a generic sparse representation ...
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    Text categorization is widely characterized as a multi-label classification problem. Robust modeling of the semantic similarity between a query text and training texts is essential to construct an effective and accurate classifier. In this paper, we systematically investigate the Web page/text classification problem via integrating sparse representation with random measurements. In particular, we first adopt a very sparse data-independent random measurement matrix to map the original high dimensional text feature space to a lower dimensional space without loss of key information. We then propose a generic sparse representation method to obtain the sparse solution by decoding the semantic correlations between the query text and entire training samples. Based on the above method, we also design and examine a series of rules by taking advantage of the sparse coefficients to propagate multiple labels for the given query texts. We have conducted extensive experiments using real-world datasets to examine our proposed approach, and the results show the effectiveness of the proposed approach.
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    Journal Title
    World Wide Web
    DOI
    https://doi.org/10.1007/s11280-017-0460-2
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Data Format not elsewhere classified
    Data Format
    Distributed Computing
    Information Systems
    Publication URI
    http://hdl.handle.net/10072/344385
    Collection
    • Journal articles

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