Perturbation Analysis of Orthogonal Least Squares

DOI10.4153/S0008439519000134OpenAlexW2925318425WikidataQ128190536 ScholiaQ128190536MaRDI QIDQ5242545

Chen, Wengu, Pengbo Geng, Huanmin Ge

Publication date: 12 November 2019

Published in: Canadian Mathematical Bulletin (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.4153/s0008439519000134

zbMATH Keywords

sparse signalrestricted isometry property (RIP)general perturbationorthogonal least square (OLS)

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Algorithms for approximation of functions (65D15) Numerical solution to inverse problems in abstract spaces (65J22)

Related Items (2)

Required Number of Iterations for Sparse Signal Recovery via Orthogonal Least Squares ⋮ A new sufficient condition for sparse recovery with multiple orthogonal least squares

Uses Software

• CoSaMP

Cites Work

• CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
• Stability and Robustness of Weak Orthogonal Matching Pursuits
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• Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
• Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
• Compressed Sensing and Source Separation
• Orthogonal least squares methods and their application to non-linear system identification
• Atomic Decomposition by Basis Pursuit
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• On the Recovery Limit of Sparse Signals Using Orthogonal Matching Pursuit
• Generalized Orthogonal Matching Pursuit
• Perturbation Analysis of Orthogonal Matching Pursuit
• Support Recovery With Orthogonal Matching Pursuit in the Presence of Noise
• Recovery of Sparse Signals via Generalized Orthogonal Matching Pursuit: A New Analysis
• A Sharp Condition for Exact Support Recovery With Orthogonal Matching Pursuit
• Recovery of Sparse Signals Using Multiple Orthogonal Least Squares
• Nearly Optimal Bounds for Orthogonal Least Squares
• Exact Recovery Conditions for Sparse Representations With Partial Support Information
• A Sharp Bound on RIC in Generalized Orthogonal Matching Pursuit
• Compressed sensing

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