Pages that link to "Item:Q927127"

The following pages link to The restricted isometry property and its implications for compressed sensing (Q927127):

Displaying 50 items.

• Stable recovery of low rank matrices from nuclear norm minimization (Q2343583) (← links)
• On some aspects of approximation of ridge functions (Q2344299) (← links)
• Error analysis of reweighted \(l_1\) greedy algorithm for noisy reconstruction (Q2345681) (← links)
• New analysis of manifold embeddings and signal recovery from compressive measurements (Q2347898) (← links)
• Group-theoretic constructions of erasure-robust frames (Q2348016) (← links)
• Optimal \(D\)-RIP bounds in compressed sensing (Q2353238) (← links)
• Refined analysis of sparse MIMO radar (Q2360571) (← links)
• Convergence and stability of iteratively reweighted least squares for low-rank matrix recovery (Q2360780) (← links)
• One condition for solution uniqueness and robustness of both \(\ell_1\)-synthesis and \(\ell_1\)-analysis minimizations (Q2374380) (← links)
• On polynomial chaos expansion via gradient-enhanced \(\ell_1\)-minimization (Q2375016) (← links)
• Recovery error analysis of noisy measurement in compressed sensing (Q2398228) (← links)
• Compressed sensing based on trust region method (Q2398233) (← links)
• Error bounds for compressed sensing algorithms with group sparsity: A unified approach (Q2399645) (← links)
• A new bound on the block restricted isometry constant in compressed sensing (Q2400748) (← links)
• Newly deterministic construction of compressed sensing matrices via singular linear spaces over finite fields (Q2410045) (← links)
• Non-iterative CS recovery algorithm for surveillance applications: subjective and real-time experience (Q2415873) (← links)
• A simple homotopy proximal mapping algorithm for compressive sensing (Q2425244) (← links)
• Basis adaptive sample efficient polynomial chaos (BASE-PC) (Q2425254) (← links)
• A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions (Q2425261) (← links)
• Greedy-like algorithms for the cosparse analysis model (Q2437331) (← links)
• Bounds of restricted isometry constants in extreme asymptotics: formulae for Gaussian matrices (Q2437333) (← links)
• RIPless compressed sensing from anisotropic measurements (Q2437334) (← links)
• Concentration of \(S\)-largest mutilated vectors with \(\ell_p\)-quasinorm for \(0<p\leq 1\) and its applications (Q2449220) (← links)
• Consistency of \(\ell_1\) recovery from noisy deterministic measurements (Q2450946) (← links)
• SAGA: sparse and geometry-aware non-negative matrix factorization through non-linear local embedding (Q2512905) (← links)
• Robust multi-image processing with optimal sparse regularization (Q2515365) (← links)
• Stable recovery of low-rank matrix via nonconvex Schatten \(p\)-minimization (Q2629808) (← links)
• Stable super-resolution limit and smallest singular value of restricted Fourier matrices (Q2659732) (← links)
• Smoothing strategy along with conjugate gradient algorithm for signal reconstruction (Q2660684) (← links)
• Compressive statistical learning with random feature moments (Q2664824) (← links)
• A necessary and sufficient condition for sparse vector recovery via \(\ell_1-\ell_2\) minimization (Q2667049) (← links)
• Sparse signal recovery from phaseless measurements via hard thresholding pursuit (Q2667051) (← links)
• The finite steps of convergence of the fast thresholding algorithms with \(f\)-feedbacks in compressed sensing (Q2672726) (← links)
• Robust signal recovery via \(\ell_{1-2}/ \ell_p\) minimization with partially known support (Q2681229) (← links)
• Explicit RIP matrices: an update (Q2681274) (← links)
• Convergence of the forward-backward algorithm: beyond the worst-case with the help of geometry (Q2687067) (← links)
• Comparison of the performance and reliability between improved sampling strategies for polynomial chaos expansion (Q2688361) (← links)
• A new hybrid \(l_p\)-\(l_2\) model for sparse solutions with applications to image processing (Q2691208) (← links)
• Retraction-based first-order feasible methods for difference-of-convex programs with smooth inequality and simple geometric constraints (Q2692792) (← links)
• Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing (Q2696571) (← links)
• On the sparsity of Lasso minimizers in sparse data recovery (Q2700886) (← links)
• (Q2787249) (← links)
• Constructions of compressed sensing matrices based on the subspaces of symplectic space over finite fields (Q2788564) (← links)
• Compressive Sensing (Q2789803) (← links)
• Construction of Sparse Binary Sensing Matrices Using Set Systems (Q2801915) (← links)
• Lower Bounds for Sparse Coding (Q2805739) (← links)
• Correcting data corruption errors for multivariate function approximation (Q2818244) (← links)
• Guarantees of Riemannian optimization for low rank matrix recovery (Q2818273) (← links)
• Sparse Learning for Large-Scale and High-Dimensional Data: A Randomized Convex-Concave Optimization Approach (Q2830269) (← links)
• Additive Combinatorics: With a View Towards Computer Science and Cryptography—An Exposition (Q2840793) (← links)