Titre :

Dictionary-based tensor-train sparse coding

Auteur(s) :

Boudehane, Abdelhak [Auteur]
Laboratoire des signaux et systèmes [L2S]
Zniyed, Yassine [Auteur]
Centre de Recherche en Automatique de Nancy [CRAN]
Tenenhaus, Arthur [Auteur]
Laboratoire des signaux et systèmes [L2S]
Le Brusquet, Laurent [Auteur]
Laboratoire des signaux et systèmes [L2S]
Boyer, Remy [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la manifestation scientifique :

28th European Signal Processing Conference (EUSIPCO 2020)

Ville :

Amsterdam

Pays :

Pays-Bas

Date de début de la manifestation scientifique :

2021-01-18

Mot(s)-clé(s) en anglais :

Sparse coding
High-order tensors
Tensor train
Constrained tensor decomposition
Fast algorithms

Discipline(s) HAL :

Mathématiques [math]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]

Résumé en anglais : [en]

Multidimensional signal processing is receiving a lot of interest recently due to the wide spread appearance of multidimensional signals in different applications of data science. Many of these fields rely on prior knowledge ...
Lire la suite >Multidimensional signal processing is receiving a lot of interest recently due to the wide spread appearance of multidimensional signals in different applications of data science. Many of these fields rely on prior knowledge of particular properties, such as sparsity for instance, in order to enhance the performance and the efficiency of the estimation algorithms. However, these multidimensional signals are, often, structured into high-order tensors, where the computational complexity and storage requirements become an issue for growing tensor orders. In this paper, we present a sparse-based Joint dImensionality Reduction And Factors rEtrieval (JIRAFE). More specifically, we assume that an arbitrary factor admits a decomposition into a redundant dictionary coded as a sparse matrix, called the sparse coding matrix. The goal is to estimate the sparse coding matrix in the Tensor-Train model framework.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL