Multiple graph regularized nonnegative matrix factorization

Jim Jing-Yan Wang, Halima Bensmail, Xin Gao

Research output: Contribution to journalArticlepeer-review

86 Scopus citations

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. © 2013 Elsevier Ltd. All rights reserved.
Original languageEnglish (US)
Pages (from-to)2840-2847
Number of pages8
JournalPattern Recognition
Volume46
Issue number10
DOIs
StatePublished - Oct 2013

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Software
  • Computer Vision and Pattern Recognition

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