| Autor: |
Nazih, Marouane, Minaoui, Khalid, Comon, Pierre |
| Přispěvatelé: |
Université Mohamed V - Rabat, GIPSA Pôle Géométrie, Apprentissage, Information et Algorithmes (GIPSA-GAIA), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA) |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Popis: |
Tensors may be seen as multidimensional arrays that generalize vectors and matrices to more than two dimensions. Among tensor decompositions, we are especially interested in the Canonical Polyadic (CP) tensor decomposition, which is important in numerous real-world applications, for its uniqueness and ease of interpretation of its factor matrices. In this research, we propose a new Canonical Polyadic (CP) model based on the Accelerated Proximal Gradient (PG) algorithm and through the introduction of a regularization function that incorporates the previous iterations; using a new strategy capable of efficiently conducting this incorporation. Simulation results demonstrate the better performance of the proposed approach in terms of accuracy and rapidity compared to other algorithms in the literature, especially when the swamp phenomenon occurs. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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