Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/8BB84BD1-7ED4-4E9E-9398-CB066FEEA045 | ||
| Year | 2021 | ||
| Type of Item | Conference Publication | ||
| License |
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| Bibliographic Citation | I. Siaminou, I. M. Papagiannakos, C. Kolomvakis and A. P. Liavas, "Accelerated stochastic gradient for nonnegative tensor completion and parallel implementation," in 2021 29th European Signal Processing Conference (EUSIPCO), Dublin, Ireland, 2021, pp. 1790-1794, doi: 10.23919/EUSIPCO54536.2021.9616067. https://doi.org/10.23919/EUSIPCO54536.2021.9616067 | ||
| Appears in Collections |
We consider the problem of nonnegative tensor completion. We adopt the alternating optimization framework and solve each nonnegative matrix completion problem via a stochastic variation of the accelerated gradient algorithm. We experimentally test the effectiveness and the efficiency of our algorithm using both real-world and synthetic data. We develop a shared-memory implementation of our algorithm using the multithreaded API OpenMP, which attains significant speedup. We believe that our approach is a very competitive candidate for the solution of very large nonnegative tensor completion problems.
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