Nesterov-based parallel algorithm for large-scale nonnegative tensor factorization
Athanasios P. Liavas, Georgios Kostoulas, Georgios Lourakis, Kejun Huang, Nicholas D. Sidiropoulos
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 (AO) 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 speedup attained by itsMessage Passing Interface implementation on a parallel computing environment. It turns out that the attained speedup is significant, rendering our algorithm a competitive candidate for the solution of very large-scale dense nonnegative tensor factorization problems.
BibTeX
@inproceedings{icassp2017_nesterovbasedpar,
title = {Nesterov-based parallel algorithm for large-scale nonnegative tensor factorization},
author = {Athanasios P. Liavas and Georgios Kostoulas and Georgios Lourakis and Kejun Huang and Nicholas D. Sidiropoulos},
booktitle = {ICASSP 2017},
year = {2017}
}