source code of my paper "Multiple graph regularized nonnegative matrix factorization"
example of using the code is at https://github.com/jingyanwang/MultiGrNMF/blob/master/MultiGrNMF_Example_4_8_a.m
Jim Jing-Yan Wang, Halima Bensmail, Xin Gao,
Multiple graph regularized nonnegative matrix factorization,
Pattern Recognition,
Volume 46, Issue 10,
2013,
Pages 2840-2847,
ISSN 0031-3203,
https://doi.org/10.1016/j.patcog.2013.03.007.
(http://www.sciencedirect.com/science/article/pii/S0031320313001362)
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.
Keywords: Data representation; Nonnegative matrix factorization; Graph Laplacian; Ensemble manifold regularization