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Nesterov-based alternating optimization for nonnegative tensor factorization: algorithm and parallel implementation

Liavas Athanasios, Kostoulas Georgios, Lourakis Georgios, Huang Kejun, Sidiropoulos Nikolaos

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URI: http://purl.tuc.gr/dl/dias/2D05F606-529A-4203-A0BB-2E97F3A568AD
Year 2018
Type of Item Peer-Reviewed Journal Publication
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Bibliographic Citation A.P. Liavas, G. Kostoulas, G. Lourakis, K. Huang and N.D. Sidiropoulos, "Nesterov-based alternating optimization for nonnegative tensor factorization: algorithm and parallel implementation," IEEE Trans. Signal Process., vol. 66, no. 4, pp. 944-953, Feb. 2018. doi: 10.1109/TSP.2017.2777399
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Summary

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.

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