Fast learning of fast transforms, with guarantees - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Fast learning of fast transforms, with guarantees

Résumé

Approximating a matrix by a product of few sparse factors whose supports possess the butterfly structure, which is common to many fast transforms, is key to learn fast transforms and speedup algorithms for inverse problems. We introduce a hierarchical approach, that recursively approximates the considered matrix into two factors. Using recent advances on the well-posedness and tractability of the two-factor fixedsupport sparse matrix factorization problem, we establish exact recovery guarantees for the proposed algorithm. Experiments show that the speed and accuracy of the factorization can be jointly improved by several orders of magnitude, compared to gradient-based optimization methods. This paper is associated to code for reproducible research available at https://hal.inria.fr/hal-03552956
Fichier principal
Vignette du fichier
main2022.pdf (287.29 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03438881 , version 1 (22-11-2021)
hal-03438881 , version 2 (15-02-2022)

Identifiants

  • HAL Id : hal-03438881 , version 1

Citer

Quoc-Tung Le, Léon Zheng, Elisa Riccietti, Rémi Gribonval. Fast learning of fast transforms, with guarantees. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapore, Singapore. ⟨hal-03438881v1⟩
398 Consultations
269 Téléchargements

Partager

Gmail Facebook X LinkedIn More