Sparse recovery in probability via \(l_q\)-minimization with Weibull random matrices for \(0 < q\leq 1\)
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Publication:1747365
DOI10.1007/s11766-018-3430-2zbMath1399.15045OpenAlexW2793267806MaRDI QIDQ1747365
Yi Gao, Shi-Gang Yue, Ji-Gen Peng
Publication date: 8 May 2018
Published in: Applied Mathematics. Series B (English Edition) (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11766-018-3430-2
Probability distributions: general theory (60E05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Random matrices (algebraic aspects) (15B52) Sampling theory in information and communication theory (94A20)
Related Items (2)
Stability of lq-analysis based dual frame with Weibull matrices for 0 < q ≤ 1 ⋮ On the grouping effect of the \(l_{1-2}\) models
Cites Work
- A mathematical introduction to compressive sensing
- The restricted isometry property for time-frequency structured random matrices
- Recovery of sparsest signals via \(\ell^q \)-minimization
- Perturbations of measurement matrices and dictionaries in compressed sensing
- New bounds for RIC in compressed sensing
- Compressed sensing with coherent and redundant dictionaries
- New bounds on the restricted isometry constant \(\delta _{2k}\)
- Restricted \(p\)-isometry property and its application for nonconvex compressive sensing
- Instance-optimality in probability with an \(\ell _1\)-minimization decoder
- Highly sparse representations from dictionaries are unique and independent of the sparseness measure
- The restricted isometry property and its implications for compressed sensing
- Sparse recovery by non-convex optimization - instance optimality
- Sparsest solutions of underdetermined linear systems via \( \ell _q\)-minimization for \(0<q\leqslant 1\)
- A simple proof of the restricted isometry property for random matrices
- Compressed data separation via dual frames based split-analysis with Weibull matrices
- Sharp RIP bound for sparse signal and low-rank matrix recovery
- Stability and instance optimality for Gaussian measurements in compressed sensing
- Stability and robustness of \(\ell_1\)-minimizations with Weibull matrices and redundant dictionaries
- Making Do with Less: An Introduction to Compressed Sensing
- $\ell_1$ Minimization with Noisy Data
- Restricted $q$-Isometry Properties Adapted to Frames for Nonconvex $l_q$-Analysis
- $NP/CMP$ Equivalence: A Phenomenon Hidden Among Sparsity Models $l_{0}$ Minimization and $l_{p}$ Minimization for Information Processing
- Sparse recovery with pre-Gaussian random matrices
- Compressed sensing and best 𝑘-term approximation
- Restricted isometry properties and nonconvex compressive sensing
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Decoding by Linear Programming
- Sparse representations in unions of bases
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Sparse and Redundant Representations
- Compressed Sensing and Redundant Dictionaries
- Uncertainty principles and ideal atomic decomposition
- Shifting Inequality and Recovery of Sparse Signals
- A generalized uncertainty principle and sparse representation in pairs of bases
- Sparse Approximate Solutions to Linear Systems
- Restricted Isometry Constants Where $\ell ^{p}$ Sparse Recovery Can Fail for $0≪ p \leq 1$
- Compressed sensing: a survey
- Compressed Sensing With General Frames via Optimal-Dual-Based $\ell _{1}$-Analysis
- Analysis versus synthesis in signal priors
- Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices
- The Improved Bounds of Restricted Isometry Constant for Recovery via <formula formulatype="inline"><tex Notation="TeX">$\ell_{p}$</tex> </formula>-Minimization
- Compressed sensing
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