Nonnegative matrix factorization with manifold regularization and maximum discriminant information

Wenjun Hu, Kup Sze Choi, Jianwen Tao, Yunliang Jiang, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Nonnegative matrix factorization (NMF) has been successfully used in different applications including computer vision, pattern recognition and text mining. NMF aims to decompose a data matrix into the product of two matrices (respectively denoted as the basis vectors and the encoding vectors), whose entries are constrained to be nonnegative. Unlike the ordinary NMF, we propose a novel NMF, denoted as MMNMF, which considers both geometrical information and discriminative information hidden in the data. The geometrical information is discovered by minimizing the distance among the encoding vectors, while the discriminative information is uncovered by maximizing the distance among base vectors. Clustering experiments are performed on the real-world data sets of faces, images, and documents to demonstrate the effectiveness of the proposed algorithm.
Original languageEnglish
Pages (from-to)837-846
Number of pages10
JournalInternational Journal of Machine Learning and Cybernetics
Volume6
Issue number5
DOIs
Publication statusPublished - 21 Oct 2015

Keywords

  • Clustering
  • Manifold regularization
  • Maximum information
  • Nonnegative matrix factorization

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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