Nesterov-based alternating optimization for nonnegative tensor factorization: algorithm and parallel implementationNesterov-based alternating optimization for nonnegative tensor factorization: algorithm and parallel implementation
Peer-Reviewed Journal Publication
Δημοσίευση σε Περιοδικό με Κριτές
2019-09-272018enWe 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.http://creativecommons.org/licenses/by/4.0/IEEE Transactions on Signal Processing664944-953
Liavas Athanasios
Λιαβας Αθανασιος
Kostoulas Georgios
Κωστουλας Γεωργιος
Lourakis Georgios
Λουρακης Γεωργιος
Huang Kejun
Sidiropoulos Nikolaos
Σιδηροπουλος Νικολαος
Institute of Electrical and Electronics Engineers
Nonnegative tensor factorization
Optimal first-order optimization algorithms
Parallel algorithms
Tensors