Paper Info
Title
Instance Optimal Decoding and the Restricted Isometry Property.
Abstract
In this paper, we address the question of information preservation in ill-posed, non-linear inverse problems, assuming that the measured data is close to a low-dimensional model set. We provide necessary and sufficient conditions for the existence of a so-called instance optimal decoder, i.e., that is robust to noise and modelling error. Inspired by existing results in compressive sensing, our analysis is based on a (Lower) Restricted Isometry Property (LRIP), formulated in a non-linear fashion. We also provide sufficient conditions for non-uniform recovery with random measurement operators, with a new formulation of the LRIP. We finish by describing typical strategies to prove the LRIP in both linear and non-linear cases, and illustrate our results by studying the invertibility of a one-layer neural net with random weights.
Year
DOI
Venue
2018
10.1088/1742-6596/1131/1/012002
arXiv: Information Theory
Field
DocType
Volume
Mathematical optimization,Algorithm,Inverse problem,Operator (computer programming),Decoding methods,Artificial neural network,Compressed sensing,Mathematics,Restricted isometry property
Journal
abs/1802.09905
Issue
Citations 
PageRank 
1
1
0.36
References 
Authors
11
2
Name
Order
Citations
PageRank
Nicolas Keriven1213.74
Rémi Gribonval2120783.59