Orthogonal nonnegative matrix factorization by sparsity and nuclear norm optimization

Junjun Pan, Kwok Po NG

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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
Issue number2
Publication statusPublished - 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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