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    Sparse subspace representation for spectral document clustering

    189256.pdf (241.9Kb)
    Access Status
    Open access
    Authors
    Budhaditya, S.
    Phung, D.
    Pham, DucSon
    Venkatesh, S.
    Date
    2012
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Budhaditya, Saha and Phung, Dinh and Pham, Duc Son and Venkatesh, Svetha. 2012. Sparse subspace representation for spectral document clustering, in Zaki, M.J. and Siebes, A. and Yu, J.X. and Goethals, B. and Wu, X. (ed), 2012 IEEE 12th International Conference on Data Mining, Dec 10-13 2012, pp. 1902-1907. Brussels, Belgium: IEEE.
    Source Title
    Proceedings of the IEEE International Conference on Data Mining (ICDM)
    Source Conference
    The IEEE International Conference on Data Mining
    DOI
    10.1109/ICDM.2012.46
    ISSN
    1550-4786
    Remarks

    Copyright © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/4290
    Collection
    • Curtin Research Publications
    Abstract

    We present a novel method for document clustering using sparse representation of documents in conjunction with spectral clustering. An ℓ1-norm optimization formulation is posed to learn the sparse representation of each document, allowing us to characterize the affinity between documents by considering the overall information instead of traditional pair wise similarities. This document affinity is encoded through a graph on which spectral clustering is performed. The decomposition into multiple subspaces allows documents to be part of a sub-group that shares a smaller set of similar vocabulary, thus allowing for cleaner clusters. Extensive experimental evaluations on two real-world datasets from Reuters-21578 and 20Newsgroup corpora show that our proposed method consistently outperforms state-of-the-art algorithms. Significantly, the performance improvement over other methods is prominent for this datasets.

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