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    An efficient nonnegative matrix factorization approach in flexible Kernel space

    133826_15582_zhang_liu.pdf (520.5Kb)
    Access Status
    Open access
    Authors
    Zhang, D.
    Liu, Wan-quan
    Date
    2009
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Zhang, Daoqiang and Liu, Wanquan. 2009. An efficient nonnegative matrix factorization approach in flexible Kernel space, in Craig Boutilier (ed), IJCAI-09, Jul 11 2009, pp. 1345-1350. Pasadena, California, USA: Morgan Kaufmann Publishers Inc.
    Source Title
    Proceedings of the 21st international joint conference on Artifical intelligence
    Source Conference
    IJCAI-09
    Additional URLs
    http://portal.acm.org/citation.cfm?id=1661661#
    ISBN
    9781577354260
    Faculty
    School of Science and Computing
    Department of Computing
    Faculty of Science and Engineering
    URI
    http://hdl.handle.net/20.500.11937/31709
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we propose a general formulation for kernel nonnegative matrix factorization with flexible kernels. Specifically, we propose the Gaussian nonnegative matrix factorization (GNMF) algorithm by using the Gaussian kernel in the framework. Different from a recently developed polynomial NMF (PNMF), GNMF finds basis vectors in the kernel-induced feature space and the computational cost is independent of input dimensions. Furthermore, we prove the convergence and nonnegativity of decomposition of our method. Extensive experiments compared with PNMF and other NMF algorithms on several face databases, validate the effectiveness of the proposed method.

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