KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computational Bioscience Research Center (CBRC)
Computer Science Program
Structural and Functional Bioinformatics Group
Permanent link to this recordhttp://hdl.handle.net/10754/562986
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AbstractNon-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.
CitationWang, 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
SponsorsThe 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.