Multiway data analysis: nonnegative tensor factorization algorithms and parallel implementationsMultiway data analysis: nonnegative tensor factorization algorithms and parallel implementationsΑνάλυση πολυδιάστατων δεδομένων: αλγόριθμοι μη αρνητικής παραγοντοποίησης τανιστών και παράλληλες υλοποιήσεις Μεταπτυχιακή Διατριβή Master Thesis 2016-08-302016enWe 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 two parallel implementations of the algorithm, with and without data replication. We test the efficiency of the algorithm in extensive numerical experiments and measure the attained speedup in a parallel 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.http://creativecommons.org/licenses/by/4.0/Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών ΥπολογιστώνKostoulas_Georgios_MSc_2016.pdfChania [Greece]Library of TUC2016-08-30application/pdf586.9 kBfree Kostoulas Georgios Κωστουλας Γεωργιος Liavas Athanasios Λιαβας Αθανασιος Garofalakis Minos Γαροφαλακης Μινως Karystinos Georgios Καρυστινος Γεωργιος Πολυτεχνείο Κρήτης Technical University of Crete Nonnegative tensor factorization Multiway data analysis