URI | http://purl.tuc.gr/dl/dias/285CC582-D9A2-4892-A1C7-F42A176CBE38 | - |
Identifier | https://doi.org/10.23919/EUSIPCO55093.2022.9909736 | - |
Identifier | https://ieeexplore.ieee.org/document/9909736 | - |
Language | en | - |
Extent | 5 pages | en |
Title | Nonnegative tensor completion: step-sizes for an accelerated variation of the stochastic gradient descent | en |
Creator | Liavas Athanasios | en |
Creator | Λιαβας Αθανασιος | el |
Creator | Papagiannakos Ioannis-Marios | en |
Creator | Παπαγιαννακος Ιωαννης-Μαριος | el |
Creator | Kolomvakis Christos | en |
Publisher | Institute of Electrical and Electronics Engineers | en |
Description | All authors were partially supported by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH - CREATE - INNOVATE (project code : T1EΔK − 03360). | en |
Content Summary | We consider the problem of nonnegative tensor completion. We adopt the alternating optimization framework and solve each nonnegative matrix least-squares problem via an accelerated variation of the stochastic gradient descent. The step-sizes used by the algorithm determine, to a high extent, its behavior. We propose two new strategies for the computation of step-sizes and we experimentally test their effectiveness using both synthetic and real-world data. | 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 | 2024-10-11 | - |
Date of Publication | 2022 | - |
Subject | Tensors | en |
Subject | Nonnegative tensor completion | en |
Subject | Stochastic gradient descent | en |
Subject | Accelerated gradient | en |
Subject | Step-size selection | en |
Subject | Armijo line-search | en |
Subject | Parallel algorithms | en |
Subject | OpenMP | en |
Bibliographic Citation | A. P. Liavas, I. M. Papagiannakos and C. Kolomvakis, "Nonnegative tensor completion: step-sizes for an accelerated variation of the stochastic gradient descent," in Proceedings of the 30th European Signal Processing Conference (EUSIPCO 2022), Belgrade, Serbia, 2022, pp. 1976-1980, doi: 10.23919/EUSIPCO55093.2022.9909736. | en |