Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/77044C87-425A-417E-84AA-59971B17339B | - |
| Αναγνωριστικό | https://ieeexplore.ieee.org/document/7484753/ | - |
| Αναγνωριστικό | https://doi.org/10.1109/TSP.2016.2576427 | - |
| Γλώσσα | en | - |
| Μέγεθος | 14 pages | en |
| Τίτλος | A flexible and efficient algorithmic framework for constrained matrix and tensor factorization | en |
| Δημιουργός | Huang Kejun | en |
| Δημιουργός | Sidiropoulos, N. D | en |
| Δημιουργός | Liavas Athanasios | en |
| Δημιουργός | Λιαβας Αθανασιος | el |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | We propose a general algorithmic framework for constrained matrix and tensor factorization, which is widely used in signal processing and machine learning. The new framework is a hybrid between alternating optimization (AO) and the alternating direction method of multipliers (ADMM): each matrix factor is updated in turn, using ADMM, hence the name AO-ADMM. This combination can naturally accommodate a great variety of constraints on the factor matrices, and almost all possible loss measures for the fitting. Computation caching and warm start strategies are used to ensure that each update is evaluated efficiently, while the outer AO framework exploits recent developments in block coordinate descent (BCD)-type methods which help ensure that every limit point is a stationary point, as well as faster and more robust convergence in practice. Three special cases are studied in detail: non-negative matrix/tensor factorization, constrained matrix/tensor completion, and dictionary learning. Extensive simulations and experiments with real data are used to showcase the effectiveness and broad applicability of the proposed framework. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2018-10-08 | - |
| Ημερομηνία Δημοσίευσης | 2016 | - |
| Θεματική Κατηγορία | Alternating direction method of multipliers | en |
| Θεματική Κατηγορία | Alternating optimization | en |
| Θεματική Κατηγορία | Canonical polyadic decomposition | en |
| Θεματική Κατηγορία | Constrained matrix/tensor factorization | en |
| Θεματική Κατηγορία | Dictionary learning | en |
| Θεματική Κατηγορία | Matrix/tensor completion | en |
| Θεματική Κατηγορία | Non-negative matrix/tensor factorization | en |
| Θεματική Κατηγορία | PARAFAC | en |
| Βιβλιογραφική Αναφορά | K. Huang, N. D. Sidiropoulos and A. P. Liavas, "A flexible and efficient algorithmic framework for constrained matrix and tensor factorization," IEEE Trans. Signal Process., vol. 64, no. 19, pp. 5052-5065, Oct. 2016. doi: 10.1109/TSP.2016.2576427 | en |
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