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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/6CA6D363-514F-49C7-AAE1-B15FE232BD4A | - |
| Identifier | https://ieeexplore.ieee.org/document/7953287/ | - |
| Identifier | https://doi.org/10.1109/ICASSP.2017.7953287 | - |
| Language | en | - |
| Extent | 5 pages | en |
| Title | Nesterov-based parallel algorithm for large-scale nonnegative tensor factorization | 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, N. D | en |
| 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 (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. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2018-05-08 | - |
| Date of Publication | 2017 | - |
| Subject | CANDECOMP | en |
| Subject | Constrained optimization | en |
| Subject | Nonnegative factorization | en |
| Subject | PARAFAC | 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 parallel algorithm for large-scale nonnegative tensor factorization," in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017, pp. 5895-5899. doi: 10.1109/ICASSP.2017.7953287 | en |
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