Convex nonnegative matrix factorization with manifold regularization

Wenjun Hu, Kup Sze Choi, Peiliang Wang, Yunliang Jiang, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)


Nonnegative Matrix Factorization (NMF) has been extensively applied in many areas, including computer vision, pattern recognition, text mining, and signal processing. However, nonnegative entries are usually required for the data matrix in NMF, which limits its application. Besides, while the basis and encoding vectors obtained by NMF can represent the original data in low dimension, the representations do not always reflect the intrinsic geometric structure embedded in the data. Motivated by manifold learning and Convex NMF (CNMF), we propose a novel matrix factorization method called Graph Regularized and Convex Nonnegative Matrix Factorization (GCNMF) by introducing a graph regularized term into CNMF. The proposed matrix factorization technique not only inherits the intrinsic low-dimensional manifold structure, but also allows the processing of mixed-sign data matrix. Clustering experiments on nonnegative and mixed-sign real-world data sets are conducted to demonstrate the effectiveness of the proposed method.
Original languageEnglish
Pages (from-to)94-103
Number of pages10
JournalNeural Networks
Publication statusPublished - 1 Mar 2015


  • Clustering
  • Convex nonnegative matrix factorization
  • Manifold regularization
  • Nonnegative matrix factorization

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


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