Multi-dimensional sparse structured signal approximation using split Bregman iterations
Data Structures and Algorithms
2015-03-11 v3 Machine Learning
Abstract
The paper focuses on the sparse approximation of signals using overcomplete representations, such that it preserves the (prior) structure of multi-dimensional signals. The underlying optimization problem is tackled using a multi-dimensional split Bregman optimization approach. An extensive empirical evaluation shows how the proposed approach compares to the state of the art depending on the signal features.
Cite
@article{arxiv.1303.5197,
title = {Multi-dimensional sparse structured signal approximation using split Bregman iterations},
author = {Yoann Isaac and Quentin Barthélemy and Jamal Atif and Cédric Gouy-Pailler and Michèle Sebag},
journal= {arXiv preprint arXiv:1303.5197},
year = {2015}
}
Comments
5 pages, ICASSP 2013 preprint