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29th European Signal Processing Conference (EUSIPCO)

作者： Siaminou, Ioanna Papagiannakos, Ioannis Marios Kolomvakis, Christos Liavas, Athanasios P. Tech Univ Crete Sch Elect & Comp Engn Khania Greece

ISBN: (纸本)9789082797060

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.

关键词： tensors stochastic gradient nonnegative tensor completion optimal first-order optimization algorithms parallel algorithms OpenMP

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Nesterov-Based Alternating optimization for Nonnegative Tensor Factorization: Algorithm and Parallel Implementation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第4期66卷 944-953页

作者： Liavas, Athanasios P. Kostoulas, Georgios Lourakis, Georgios Huang, Kejun Sidiropoulos, Nicholas D. Tech Univ Crete Sch Elect & Comp Engn Khania 73100 Greece Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

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 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 attained speedup in a multicore computing environment. It turns out that the derived algorithm is a competitive candidate for the solution of very large-scale dense nonnegative tensor factorization problems.

关键词： Tensors nonnegative tensor factorization optimal first-order optimization algorithms parallel algorithms

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Nesterov-based Alternating optimization for Nonnegative Tensor Completion: Algorithm and Parallel Implementation 19

Nesterov-based Alternating Optimization for Nonnegative Tens...

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IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Lourakis, Georgios Liavas, Athanasios P. Tech Univ Crete Sch Elect & Comp Engn Khania Greece

ISBN: (纸本)9781538635124

We consider the problem of nonnegative tensor completion. Our aim is to derive an efficient algorithm that is also suitable for parallel implementation. We adopt the alternating optimization framework and solve each nonnegative matrix completion problem via a Nesterov-type algorithm for smooth convex problems. We describe a parallel implementation of the algorithm and measure the attained speedup in a multi-core computing environment. It turns out that the derived algorithm is an efficient candidate for the solution of very large-scale sparse nonnegative tensor completion problems.

关键词： tensors nonnegative tensor completion optimal first-order optimization algorithms parallel algorithms

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