Robust recovery of signals with partially known support information using weighted BPDN
From MaRDI portal
Publication:5132234
DOI10.1142/S0219530520500062zbMath1458.94141OpenAlexW3034478065MaRDI QIDQ5132234
Publication date: 10 November 2020
Published in: Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0219530520500062
Convex programming (90C25) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Linear operators and ill-posed problems, regularization (47A52)
Related Items
Perturbation analysis of \(L_{1-2}\) method for robust sparse recovery, Robust signal recovery for ℓ 1–2 minimization via prior support information
Uses Software
Cites Work
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- A mathematical introduction to compressive sensing
- The restricted isometry property and its implications for compressed sensing
- Signal recovery under cumulative coherence
- Recovery of signals under the condition on RIC and ROC via prior support information
- The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso)
- A short note on compressed sensing with partially known signal support
- Sparse recovery with coherent tight frames via analysis Dantzig selector and analysis LASSO
- Optimal RIP bounds for sparse signals recovery via \(\ell_p\) minimization
- Stable recovery of analysis based approaches
- One condition for solution uniqueness and robustness of both \(\ell_1\)-synthesis and \(\ell_1\)-analysis minimizations
- Simultaneous analysis of Lasso and Dantzig selector
- Recovery analysis for weighted \(\ell_{1}\)-minimization using the null space property
- Deterministic sampling of sparse trigonometric polynomials
- Atomic Decomposition by Basis Pursuit
- Improved Iteratively Reweighted Least Squares for Unconstrained Smoothed $\ell_q$ Minimization
- Optimal Choice of Weights for Sparse Recovery With Prior Information
- Time Invariant Error Bounds for Modified-CS-Based Sparse Signal Sequence Recovery
- On Sparse Representations in Arbitrary Redundant Bases
- Decoding by Linear Programming
- Recovery of Exact Sparse Representations in the Presence of Bounded Noise
- Designing structured tight frames via an alternating projection method
- Stable Recovery of Sparse Signals Via Regularized Minimization
- A Proof of Conjecture on Restricted Isometry Property Constants $\delta _{tk}\ \left(0<t<\frac {4}{3}\right)$
- Modified-CS: Modifying Compressive Sensing for Problems With Partially Known Support
- Weighted ${\ell}_{{1}}$-minimization for sparse recovery under arbitrary prior information
- Weighted LASSO for Sparse Recovery With Statistical Prior Support Information
- A Data-Dependent Weighted LASSO Under Poisson Noise
- Minimization of $\ell_{1-2}$ for Compressed Sensing
- Recovering Compressively Sampled Signals Using Partial Support Information
- Stable Recovery of Sparse Signals and an Oracle Inequality
- Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices
- Compressed sensing