Multiple graph regularized nonnegative matrix factorization

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.

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

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.

Elsevier BV

Pattern Recognition


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