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    Multiple graph regularized nonnegative matrix factorization

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    Type
    Article
    Authors
    Wang, Jim Jing-Yan
    Bensmail, Halima
    Gao, Xin cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Structural and Functional Bioinformatics Group
    Date
    2013-10
    Permanent link to this record
    http://hdl.handle.net/10754/562986
    
    Metadata
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    Abstract
    Non-negative matrix factorization (NMF) has been widely used as a data representation method based on components. To overcome the disadvantage of NMF in failing to consider the manifold structure of a data set, graph regularized NMF (GrNMF) has been proposed by Cai et al. by constructing an affinity graph and searching for a matrix factorization that respects graph structure. Selecting a graph model and its corresponding parameters is critical for this strategy. This process is usually carried out by cross-validation or discrete grid search, which are time consuming and prone to overfitting. In this paper, we propose a GrNMF, called MultiGrNMF, in which the intrinsic manifold is approximated by a linear combination of several graphs with different models and parameters inspired by ensemble manifold regularization. Factorization metrics and linear combination coefficients of graphs are determined simultaneously within a unified object function. They are alternately optimized in an iterative algorithm, thus resulting in a novel data representation algorithm. Extensive experiments on a protein subcellular localization task and an Alzheimer's disease diagnosis task demonstrate the effectiveness of the proposed algorithm. © 2013 Elsevier Ltd. All rights reserved.
    Citation
    Wang, J. J.-Y., Bensmail, H., & Gao, X. (2013). Multiple graph regularized nonnegative matrix factorization. Pattern Recognition, 46(10), 2840–2847. doi:10.1016/j.patcog.2013.03.007
    Sponsors
    The study was supported by grants from 2011 Qatar Annual Research Forum Award (Grant No. ARF2011) and King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
    Publisher
    Elsevier BV
    Journal
    Pattern Recognition
    DOI
    10.1016/j.patcog.2013.03.007
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.patcog.2013.03.007
    Scopus Count
    Collections
    Articles; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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