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 language | English (US) |
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Article number | 8119874 |
Pages (from-to) | 944-953 |
Number of pages | 10 |
Journal | IEEE Transactions on Signal Processing |
Volume | 66 |
Issue number | 4 |
DOIs | |
State | Published - Feb 15 2018 |
Bibliographical note
Publisher Copyright:© 1991-2012 IEEE.
Keywords
- Tensors
- nonnegative tensor factorization
- optimal first-order optimization algorithms
- parallel algorithms