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Nesterov-based parallel algorithm for large-scale nonnegative tensor factorization

Liavas Athanasios, Kostoulas Georgios, Lourakis Georgios, Huang Kejun, Sidiropoulos, N. D

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URIhttp://purl.tuc.gr/dl/dias/6CA6D363-514F-49C7-AAE1-B15FE232BD4A-
Identifierhttps://ieeexplore.ieee.org/document/7953287/-
Identifierhttps://doi.org/10.1109/ICASSP.2017.7953287-
Languageen-
Extent5 pagesen
TitleNesterov-based parallel algorithm for large-scale nonnegative tensor factorizationen
CreatorLiavas Athanasiosen
CreatorΛιαβας Αθανασιοςel
CreatorKostoulas Georgiosen
CreatorΚωστουλας Γεωργιοςel
CreatorLourakis Georgiosen
CreatorΛουρακης Γεωργιοςel
CreatorHuang Kejunen
CreatorSidiropoulos, N. Den
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryWe 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 (AO) 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 speedup attained by itsMessage Passing Interface implementation on a parallel computing environment. It turns out that the attained speedup is significant, rendering our algorithm a competitive candidate for the solution of very large-scale dense nonnegative tensor factorization problems.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2018-05-08-
Date of Publication2017-
SubjectCANDECOMPen
SubjectConstrained optimizationen
SubjectNonnegative factorizationen
SubjectPARAFACen
SubjectParallel algorithmsen
SubjectTensorsen
Bibliographic CitationA. P. Liavas, G. Kostoulas, G. Lourakis, K. Huang and N. D. Sidiropoulos, "Nesterov-based parallel algorithm for large-scale nonnegative tensor factorization," in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017, pp. 5895-5899. doi: 10.1109/ICASSP.2017.7953287en

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