Sparse recovery based on the generalized error function
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Publication:6616997
DOI10.4208/jcm.2204-m2021-0288MaRDI QIDQ6616997
Publication date: 9 October 2024
Published in: Journal of Computational Mathematics (Search for Journal in Brave)
sparse recoverynonconvex regularizationgeneralized error functiondifference of convex functions algorithmsiterative reweighted L1
Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Sampling theory in information and communication theory (94A20)
Cites Work
- Unnamed Item
- Unnamed Item
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- Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
- Nearly unbiased variable selection under minimax concave penalty
- Nonlinear total variation based noise removal algorithms
- DC approximation approaches for sparse optimization
- A mathematical introduction to compressive sensing
- Theory of compressive sensing via \(\ell_1\)-minimization: a non-RIP analysis and extensions
- Statistics for high-dimensional data. Methods, theory and applications.
- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- One-step sparse estimates in nonconcave penalized likelihood models
- Sparsest solutions of underdetermined linear systems via \( \ell _q\)-minimization for \(0<q\leqslant 1\)
- Convex analysis approach to d. c. programming: Theory, algorithms and applications
- Folded concave penalized sparse linear regression: sparsity, statistical performance, and algorithmic theory for local solutions
- Fast L1-L2 minimization via a proximal operator
- Minimization of transformed \(L_1\) penalty: theory, difference of convex function algorithm, and robust application in compressed sensing
- On the Lambert \(w\) function
- On the conditions used to prove oracle results for the Lasso
- A class of null space conditions for sparse recovery via nonconvex, non-separable minimizations
- A novel regularization based on the error function for sparse recovery
- On the $O(1/n)$ Convergence Rate of the DouglasâRachford Alternating Direction Method
- Robustness of Sparse Recovery via <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>-Minimization: A Topological Viewpoint
- Compressed sensing and best đ-term approximation
- SparseNet: Coordinate Descent With Nonconvex Penalties
- On some inequalities for the incomplete gamma function
- Truncated $l_{1-2}$ Models for Sparse Recovery and Rank Minimization
- The Split Bregman Method for L1-Regularized Problems
- Compressed sensing recovery via nonconvex shrinkage penalties
- A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing
- Restricted isometry properties and nonconvex compressive sensing
- A D.C. Optimization Algorithm for Solving the Trust-Region Subproblem
- The Concave-Convex Procedure
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed $\ell ^{0}$ Norm
- Recovery of Low-Rank Matrices Under Affine Constraints via a Smoothed Rank Function
- Iterative Concave Rank Approximation for Recovering Low-Rank Matrices
- Successive Concave Sparsity Approximation for Compressed Sensing
- Sparse Approximate Solutions to Linear Systems
- Reweighted $\ell_1$-Minimization for Sparse Solutions to Underdetermined Linear Systems
- Edge Guided Reconstruction for Compressive Imaging
- Accelerated Schemes for the $L_1/L_2$ Minimization
- SPOQ $\ell _p$-Over-$\ell _q$ Regularization for Sparse Signal Recovery Applied to Mass Spectrometry
- A Scale-Invariant Approach for Sparse Signal Recovery
- Minimization of $\ell_{1-2}$ for Compressed Sensing
- On Iteratively Reweighted Algorithms for Nonsmooth Nonconvex Optimization in Computer Vision
- Difference-of-Convex Learning: Directional Stationarity, Optimality, and Sparsity
- Compressed sensing
- A general theory of concave regularization for high-dimensional sparse estimation problems
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