Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/2D05F606-529A-4203-A0BB-2E97F3A568AD | - |
| Identifier | https://doi.org/10.1109/TSP.2017.2777399 | - |
| Identifier | https://ieeexplore.ieee.org/document/8119874 | - |
| Language | en | - |
| Extent | 10 pages | en |
| Title | Nesterov-based alternating optimization for nonnegative tensor factorization: algorithm and parallel implementation | en |
| Creator | Liavas Athanasios | en |
| Creator | Λιαβας Αθανασιος | el |
| Creator | Kostoulas Georgios | en |
| Creator | Κωστουλας Γεωργιος | el |
| Creator | Lourakis Georgios | en |
| Creator | Λουρακης Γεωργιος | el |
| Creator | Huang Kejun | en |
| Creator | Sidiropoulos Nikolaos | en |
| Creator | Σιδηροπουλος Νικολαος | el |
| Publisher | Institute of Electrical and Electronics Engineers | en |
| Content Summary | 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. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2019-09-27 | - |
| Date of Publication | 2018 | - |
| Subject | Nonnegative tensor factorization | en |
| Subject | Optimal first-order optimization algorithms | en |
| Subject | Parallel algorithms | en |
| Subject | Tensors | en |
| Bibliographic Citation | A.P. Liavas, G. Kostoulas, G. Lourakis, K. Huang and N.D. Sidiropoulos, "Nesterov-based alternating optimization for nonnegative tensor factorization: algorithm and parallel implementation," IEEE Trans. Signal Process., vol. 66, no. 4, pp. 944-953, Feb. 2018. doi: 10.1109/TSP.2017.2777399 | en |
Copyright © DIAS 2013
