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Paper Detail

Paper IDG.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} &

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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