Online Nonnegative Canonical Polyadic Decomposition: Algorithms and Application
| Autor: | Sanou, Isaac Wilfried, Redon, Roland, Luciani, Xavier, Stéphane Jean Louis MOUNIER |
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| Přispěvatelé: | Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Institut méditerranéen d'océanologie (MIO), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | 2021 29th European Signal Processing Conference (EUSIPCO) 2021 29th European Signal Processing Conference (EUSIPCO), Aug 2021, Dublin, Ireland. pp.1805-1809, ⟨10.23919/EUSIPCO54536.2021.9616028⟩ Web of Science |
| DOI: | 10.23919/eusipco54536.2021.9616028 |
| Popis: | International audience; The Nonnegative Canonical Polyadic Decomposition (NN-CPD) is now widely used in signal processing to decompose multi-way arrays thanks to nonnegative factor matrices. In many applications, a three way array is built from collections of 2Dsignals and new signals are regularly recorded. In this case one may want to update the factor matrices after each new measurement without computing the NN-CPD of the whole array. We then speak of Online NN-CPD. In this context the main difficulty is that the number of relevant factors is unknown and can vary with time. In this paper we propose two algorithms to compute the Online NN-CPD based on sparse dictionary learning. We also introduce an application example of Online NN-CPD in environmental sciences and evaluate the performances of the proposed approach in this context on real data. |
| Databáze: | OpenAIRE |
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