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Electrical Engineering and Systems Science > Signal Processing

arXiv:2109.09534 (eess)
[Submitted on 20 Sep 2021]

Title:Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation

Authors:Ioanna Siaminou, Ioannis Marios Papagiannakos, Christos Kolomvakis, Athanasios P. Liavas
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Abstract: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 multi-threaded 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.
Comments: 5 pages, 4 figures, this work was accepted and presented at EUSIPCO 2021
Subjects: Signal Processing (eess.SP); Machine Learning (cs.LG)
Cite as: arXiv:2109.09534 [eess.SP]
  (or arXiv:2109.09534v1 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.2109.09534

Submission history

From: Ioannis Marios Papagiannakos Nikolaos [view email]
[v1] Mon, 20 Sep 2021 13:32:12 UTC (4,068 KB)
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