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    Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

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    Type
    Article
    Authors
    Wang, Jim Jing-Yan
    Huang, Jianhua Z.
    Sun, Yijun
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Structural and Functional Bioinformatics Group
    Date
    2014-09-20
    Online Publication Date
    2014-09-20
    Print Publication Date
    2015-02
    Permanent link to this record
    http://hdl.handle.net/10754/552351
    
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    Abstract
    Nonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.
    Citation
    Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization 2015, 42 (3):1278 Expert Systems with Applications
    Publisher
    Elsevier BV
    Journal
    Expert Systems with Applications
    DOI
    10.1016/j.eswa.2014.09.008
    Additional Links
    http://linkinghub.elsevier.com/retrieve/pii/S0957417414005478
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.eswa.2014.09.008
    Scopus Count
    Collections
    Articles; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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