2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | G.3.1 | ||
| Paper Title | Optimal Restricted Isometry Condition for Exact Sparse Recovery with Orthogonal Least Squares | ||
| Authors | Junhan Kim, Byonghyo Shim, Seoul National University, Korea (South) | ||
| Session | G.3: Compressed Sensing | ||
| Presentation | Lecture | ||
| Track | Graphs, Games, Sparsity, and Signal Processing | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract |
Orthogonal least squares (OLS) is a classic algorithm for sparse recovery, function approximation, and subset selection. In this paper, we analyze the performance guarantee of the OLS algorithm. Specifically, we show that OLS guarantees the exact reconstruction of any $K$-sparse vector in $K$ iterations, provided that a sensing matrix has unit $\ell_{2}$-norm columns and satisfies the restricted isometry property (RIP) of order $K+1$ with \begin{align*} \delta_{K+1} & | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
