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Accelerated stochastic gradient for nonnegative tensor completion and parallel implementation

Siaminou Ioanna, Papagiannakos Ioannis-Marios, Kolomvakis Christos, Liavas Athanasios

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URI: http://purl.tuc.gr/dl/dias/8BB84BD1-7ED4-4E9E-9398-CB066FEEA045
Year 2021
Type of Item Conference Publication
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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
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Summary

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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