Multivariate Convolutional Sparse Coding with Low Rank Tensor - Université Sorbonne Paris Nord Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2019

Multivariate Convolutional Sparse Coding with Low Rank Tensor

Résumé

This paper introduces a new multivariate convolutional sparse coding based on tensor algebra with a general model enforcing both element-wise sparsity and low-rankness of the activations tensors. By using the CP decomposition, this model achieves a significantly more efficient encoding of the multivariate signal-particularly in the high order/ dimension setting-resulting in better performance. We prove that our model is closely related to the Kruskal tensor regression problem, offering interesting theoretical guarantees to our setting. Furthermore, we provide an efficient optimization algorithm based on alternating optimization to solve this model. Finally, we evaluate our algorithm with a large range of experiments, highlighting its advantages and limitations.
Fichier principal
Vignette du fichier
Multivariate_Convolutional_Sparse_Coding_with_Low_Rank_Tensor.pdf (1.85 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-02196166 , version 1 (09-08-2019)

Identifiants

Citer

Pierre Humbert, Julien Audiffren, Laurent Oudre, Nicolas Vayatis. Multivariate Convolutional Sparse Coding with Low Rank Tensor. 2019. ⟨hal-02196166⟩
112 Consultations
152 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More