Multiple graph regularized nonnegative matrix factorization

作者：Wang Jim Jing Yan; Bensmail Halima; Gao Xin^*

来源：Pattern Recognition, 2013, 46(10): 2840-2847.

DOI：10.1016/j.patcog.2013.03.007

• Data representation
• Nonnegative matrix factorization
• Graph Laplacian
• Ensemble manifold regularization

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%26apos;s disease diagnosis task demonstrate the effectiveness of the proposed algorithm.

• 出版日期2013-10