The following pages link to Decoding by Linear Programming (Q3546644):
Displaying 50 items.
- Sparse signal recovery via non-convex optimization and overcomplete dictionaries (Q4554319) (← links)
- Deterministic construction of compressed sensing matrices with characters over finite fields (Q4554544) (← links)
- (Q4558139) (← links)
- (Q4558147) (← links)
- (Q4558493) (← links)
- A Tight Bound of Hard Thresholding (Q4558539) (← links)
- Iterative positive thresholding algorithm for non-negative sparse optimization (Q4559400) (← links)
- A General Theory of Singular Values with Applications to Signal Denoising (Q4561328) (← links)
- Sparsest representations and approximations of an underdetermined linear system (Q4569345) (← links)
- Multicompartment magnetic resonance fingerprinting (Q4582674) (← links)
- An efficient superpixel-based sparse representation framework for hyperspectral image classification (Q4595573) (← links)
- CGIHT: conjugate gradient iterative hard thresholding for compressed sensing and matrix completion (Q4603591) (← links)
- Guarantees of total variation minimization for signal recovery (Q4603592) (← links)
- Time for dithering: fast and quantized random embeddings via the restricted isometry property (Q4603715) (← links)
- Super-resolution of point sources via convex programming (Q4603727) (← links)
- A null-space-based weighted<i>l</i><sub>1</sub>minimization approach to compressed sensing (Q4603732) (← links)
- Proximal Mapping for Symmetric Penalty and Sparsity (Q4606656) (← links)
- Flavors of Compressive Sensing (Q4609797) (← links)
- Batched Stochastic Gradient Descent with Weighted Sampling (Q4609808) (← links)
- Sliced-Inverse-Regression--Aided Rotated Compressive Sensing Method for Uncertainty Quantification (Q4611526) (← links)
- (Q4614089) (← links)
- Frames for compressed sensing using coherence (Q4618389) (← links)
- Hierachical Bayesian models and sparsity: <i>ℓ</i> <sub>2</sub> -magic (Q4625206) (← links)
- (Q4633018) (← links)
- Consistency Analysis for Massively Inconsistent Datasets in Bound-to-Bound Data Collaboration (Q4636381) (← links)
- Inverse scale space decomposition (Q4638183) (← links)
- Greedy forward regression for variable screening (Q4639813) (← links)
- Tight and full spark Chebyshev frames with real entries and worst-case coherence analysis (Q4686244) (← links)
- What is a Frame? Theory and Applications of Frames (Q4686734) (← links)
- A Multiple Measurement Vector Approach to Synthetic Aperture Radar Imaging (Q4686935) (← links)
- Spherical Designs and Nonconvex Minimization for Recovery of Sparse Signals on the Sphere (Q4689765) (← links)
- An adaptive inverse scale space method for compressed sensing (Q4911904) (← links)
- $\ell _0$ Minimization for wavelet frame based image restoration (Q4912013) (← links)
- ELASTIC-NET REGULARIZATION FOR LOW-RANK MATRIX RECOVERY (Q4917269) (← links)
- Estimator of prediction error based on approximate message passing for penalized linear regression (Q4964647) (← links)
- Welch bound-achieving compressed sensing matrices from optimal codebooks (Q4965909) (← links)
- (Q4969095) (← links)
- (Q4969108) (← links)
- Deterministic Construction of Compressed Sensing Matrices from Codes (Q4977889) (← links)
- Decoupling noise and features via weighted ℓ <sub>1</sub> -analysis compressed sensing (Q4981900) (← links)
- Stochastic Collocation Methods via Minimisation of the Transformed L<sub>1</sub>-Penalty (Q4985226) (← links)
- Bayesian sparse regularization for multiple force identification and location in time domain (Q4990742) (← links)
- Binary sparse signal recovery with binary matching pursuit <sup>*</sup> (Q4993901) (← links)
- Sparse Solutions by a Quadratically Constrained ℓq (0 <<i>q</i>< 1) Minimization Model (Q4995085) (← links)
- Analysis and Algorithms for Some Compressed Sensing Models Based on L1/L2 Minimization (Q4997175) (← links)
- (Q4998877) (← links)
- (Q4999097) (← links)
- <b> <i>ℓ</i> <sub>1</sub> − <i>αℓ</i> <sub>2</sub> </b> minimization methods for signal and image reconstruction with impulsive noise removal (Q5000605) (← links)
- The Dantzig selector: recovery of signal via ℓ <sub>1</sub> − αℓ <sub>2</sub> minimization (Q5014489) (← links)
- A new sufficient condition for sparse vector recovery via ℓ1 − ℓ2 local minimization (Q5016825) (← links)
