Fast nonnegative tensor factorization by using accelerated proximal gradient

Guoxu Zhou, Qibin Zhao, Yu Zhang, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Nonnegative tensor factorization (NTF) has been widely applied in high-dimensional nonnegative tensor data analysis. However, existing algorithms suffer from slow convergence caused by the nonnegativity constraint and hence their practical applications are severely limited. By combining accelerated proximal gradient and low-rank approximation, we propose a new NTF algorithm which is significantly faster than state-of-the-art NTF algorithms.

Original languageEnglish
Pages (from-to)459-468
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 2014
Externally publishedYes


  • Accelerated proximal gradient
  • CP (PARAFAC) decompositions
  • Nonnegative tensor factorization


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