Orthogonal Nonnegative Matrix Factorization by Sparsity and Nuclear Norm Optimization

Junjun Pan, Michael K. Ng*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

16 Citations (Scopus)
253 Downloads (Pure)

Abstract

In this paper, we study orthogonal nonnegative matrix factorization. We demonstrate the coefficient matrix can be sparse and low-rank in the orthogonal nonnegative matrix factorization. By using these properties, we propose to use a sparsity and nuclear norm minimization for the factorization and develop a convex optimization model for finding the coefficient matrix in the factorization. Numerical examples including synthetic and real-world data sets are presented to illustrate the effectiveness of the proposed algorithm and demonstrate that its performance is better than other testing methods.

Original languageEnglish
Pages (from-to)856-875
Number of pages20
JournalSIAM Journal on Matrix Analysis and Applications
Volume39
Issue number2
DOIs
Publication statusPublished - 22 May 2018

Scopus Subject Areas

  • Analysis

User-Defined Keywords

  • Convex optimization
  • Document clustering
  • Hyperspectral image unmixing
  • Nuclear norm
  • Orthogonal nonnegative matrix factorization
  • Sparsity

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