Stability of lq-analysis based dual frame with Weibull matrices for 0 < q ≤ 1
From MaRDI portal
Publication:2958494
DOI10.1142/S0219691317500011zbMath1371.15037OpenAlexW2538247727MaRDI QIDQ2958494
Yongdong Huang, Yi Gao, Shi-Gang Yue
Publication date: 2 February 2017
Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0219691317500011
Random matrices (probabilistic aspects) (60B20) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Random matrices (algebraic aspects) (15B52) Frames, locales (06D22) Sampling theory in information and communication theory (94A20)
Related Items
Robust recovery of a kind of weighted l1-minimization without noise level ⋮ Recovery of low-rank matrices based on the rank null space properties ⋮ Perturbation analysis of low-rank matrix stable recovery
Cites Work
- A mathematical introduction to compressive sensing
- Sparsity and spectral properties of dual frames
- Perturbations of measurement matrices and dictionaries in compressed sensing
- Compressed sensing with coherent and redundant dictionaries
- The Gelfand widths of \(\ell_p\)-balls for \(0 < p \leq 1\)
- Restricted \(p\)-isometry property and its application for nonconvex compressive sensing
- A null space analysis of the \(\ell_1\)-synthesis method in dictionary-based compressed sensing
- 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
- Optimal dual frames for erasures
- Sparse recovery in probability via \(l_q\)-minimization with Weibull random matrices for \(0 < q\leq 1\)
- Stability and robustness of \(\ell_1\)-minimizations with Weibull matrices and redundant dictionaries
- Sparse recovery with pre-Gaussian random matrices
- Restricted isometry properties and nonconvex compressive sensing
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Decoding by Linear Programming
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Restricted Isometry Constants Where $\ell ^{p}$ Sparse Recovery Can Fail for $0≪ p \leq 1$
- Compressed Sensing With General Frames via Optimal-Dual-Based $\ell _{1}$-Analysis
- The Improved Bounds of Restricted Isometry Constant for Recovery via <formula formulatype="inline"><tex Notation="TeX">$\ell_{p}$</tex> </formula>-Minimization
- Compressed sensing