期刊
PATTERN RECOGNITION
卷 46, 期 10, 页码 2840-2847出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2013.03.007
关键词
Data representation; Nonnegative matrix factorization; Graph Laplacian; Ensemble manifold regularization
资金
- Qatar Annual Research Forum Award [ARF2011]
- King Abdullah University of Science and Technology (KAUST), Saudi Arabia
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. (C) 2013 Elsevier Ltd. All rights reserved.
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