Recovery of high-dimensional sparse signals via \(\ell_1\)-minimization

DOI10.1155/2013/636094zbMath1273.62148OpenAlexW2046688153WikidataQ59003488 ScholiaQ59003488MaRDI QIDQ364458

Limin Su, Shi-Qing Wang

Publication date: 9 September 2013

Published in: Journal of Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2013/636094

Mathematics Subject Classification ID

Multivariate analysis (62H99)

Related Items

Weaker regularity conditions and sparse recovery in high-dimensional regression

Cites Work

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• The oracle inequalities on simultaneous Lasso and Dantzig selector in high-dimensional nonparametric regression
• The restricted isometry property and its implications for compressed sensing
• Simultaneous analysis of Lasso and Dantzig selector
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• On Sparse Representations in Arbitrary Redundant Bases
• Decoding by Linear Programming
• Stable recovery of sparse overcomplete representations in the presence of noise
• Recovery of Exact Sparse Representations in the Presence of Bounded Noise
• Sparse representations in unions of bases
• Greed is Good: Algorithmic Results for Sparse Approximation
• Just relax: convex programming methods for identifying sparse signals in noise
• Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
• Uncertainty principles and ideal atomic decomposition
• Shifting Inequality and Recovery of Sparse Signals
• Prox-Method with Rate of Convergence O(1/t) for Variational Inequalities with Lipschitz Continuous Monotone Operators and Smooth Convex-Concave Saddle Point Problems
• Further Results on Stable Recovery of Sparse Overcomplete Representations in the Presence of Noise
• On Recovery of Sparse Signals Via $\ell _{1}$ Minimization
• New Bounds for Restricted Isometry Constants
• Stable Recovery of Sparse Signals and an Oracle Inequality
• For most large underdetermined systems of linear equations the minimal 𝓁₁‐norm solution is also the sparsest solution
• Stable signal recovery from incomplete and inaccurate measurements