Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/4163C723-F3A5-4838-BEAF-2A8104F02EB3 | ||
| Year | 2021 | ||
| Type of Item | Conference Publication | ||
| License |
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| Bibliographic Citation | I. 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. https://doi.org/10.23919/EUSIPCO54536.2021.9616029 | ||
| Appears in Collections |
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.
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