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An accelerated stochastic gradient for canonical polyadic decomposition

Siaminou Ioanna, Liavas Athanasios

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URIhttp://purl.tuc.gr/dl/dias/4163C723-F3A5-4838-BEAF-2A8104F02EB3-
Identifierhttps://doi.org/10.23919/EUSIPCO54536.2021.9616029-
Identifierhttps://ieeexplore.ieee.org/document/9616029-
Languageen-
Extent5 pagesen
TitleAn accelerated stochastic gradient for canonical polyadic decompositionen
CreatorSiaminou Ioannaen
CreatorΣιαμινου Ιωανναel
CreatorLiavas Athanasiosen
CreatorΛιαβας Αθανασιοςel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryWe consider the problem of structured canonical polyadic decomposition. If the size of the problem is very big, then stochastic gradient approaches are viable alternatives to classical methods, such as Alternating Optimization and All-At-Once optimization. We extend a recent stochastic gradient approach by employing an acceleration step (Nesterov momentum) in each iteration. We compare our approach with state-of-the-art alternatives, using both synthetic and real-world data, and find it to be very competitive.en
Type of ItemΔημοσίευση σε Συνέδριοel
Type of ItemConference Publicationen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-05-26-
Date of Publication2021-
SubjectTensor factorizationen
SubjectStochastic optimizationen
SubjectCPD/PARAFACen
SubjectNesterov accelerationen
Bibliographic CitationI. Siaminou and A. P. Liavas, "An accelerated stochastic gradient for canonical polyadic decomposition," in 2021 29th European Signal Processing Conference (EUSIPCO), Dublin, Ireland, 2021, pp. 1785-1789, doi: 10.23919/EUSIPCO54536.2021.9616029.en

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