Nesterov-Based Alternating Optimization for Nonnegative Tensor Factorization: Algorithm and Parallel Implementation

Athanasios P. Liavas, Georgios Kostoulas, Georgios Lourakis, Kejun Huang, Nicholas D. Sidiropoulos

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

We consider the problem of nonnegative tensor factorization. Our aim is to derive an efficient algorithm that is also suitable for parallel implementation. We adopt the alternating optimization framework and solve each matrix nonnegative least-squares problem via a Nesterov-Type algorithm for strongly convex problems. We describe a parallel implementation of the algorithm and measure the attained speedup in a multicore computing environment. It turns out that the derived algorithm is a competitive candidate for the solution of very large-scale dense nonnegative tensor factorization problems.

Original languageEnglish (US)
Article number8119874
Pages (from-to)944-953
Number of pages10
JournalIEEE Transactions on Signal Processing
Volume66
Issue number4
DOIs
StatePublished - Feb 15 2018

Keywords

  • Tensors
  • nonnegative tensor factorization
  • optimal first-order optimization algorithms
  • parallel algorithms

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