Representation recovery via \(L_1\)-norm minimization with corrupted data
From MaRDI portal
Publication:6199567
DOI10.1016/j.ins.2021.11.074OpenAlexW4200374718MaRDI QIDQ6199567
Shen-Shyang Ho, Hiok Chai Quek, Woon Huei Chai
Publication date: 28 February 2024
Published in: Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ins.2021.11.074
error correctionsparse recoverysparse representationGaussian matrix\(L_1\)-norm minimizationrecovery probabilityYALL1
Cites Work
- Unnamed Item
- Unnamed Item
- On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
- Robust sparse signal reconstructions against basis mismatch and their applications
- Sparse illumination learning and transfer for single-sample face recognition with image corruption and misalignment
- Rapid compressed sensing reconstruction: a semi-tensor product approach
- Adaptive decomposition-based evolutionary approach for multiobjective sparse reconstruction
- Salt and pepper noise removal with multi-class dictionary learning and L\(_0\) norm regularizations
- A theoretical result of sparse signal recovery via alternating projection method
- Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
- On Sparse Representations in Arbitrary Redundant Bases
- Robust Linear Regression via $\ell_0$ Regularization
- Sparse Approximate Solutions to Linear Systems
- Solving Linear Programs in the Current Matrix Multiplication Time
- Dense Error Correction Via $\ell^1$-Minimization
- Sparse Representation With Kernels
- Stable signal recovery from incomplete and inaccurate measurements
- Compressed sensing
This page was built for publication: Representation recovery via \(L_1\)-norm minimization with corrupted data
