An accelerated stochastic gradient for canonical polyadic decompositionAn accelerated stochastic gradient for canonical polyadic decomposition Δημοσίευση σε Συνέδριο Conference Publication 2023-05-262021enAll members were supported by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH - CREATE - INNOVATE (project code : T1EΔK - 03360).We 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.http://creativecommons.org/licenses/by/4.0/1785-17892021 29th European Signal Processing ConferenceSiaminou_et_al_EUSIPCO_2021.pdfChania [Greece]Library of TUC2023-05-26application/pdf605.9 kBfree Siaminou Ioanna Σιαμινου Ιωαννα Liavas Athanasios Λιαβας Αθανασιος Institute of Electrical and Electronics Engineers Tensor factorization Stochastic optimization CPD/PARAFAC Nesterov acceleration