The following pages link to Compressed sensing (Q5900527):
Displaying 50 items.
- Optimal learning (Q6151544) (← links)
- A fixed-time converging neurodynamic approach with time-varying coefficients for \(l_1\)-minimization problem (Q6151933) (← links)
- Nonconvex \(\ell_p-\alpha\ell_q\) minimization method and \(p\)-RIP condition for stable recovery of approximately \(k\)-sparse signals (Q6151971) (← links)
- A duality approach to regularized learning problems in Banach spaces (Q6154551) (← links)
- Denoising low-rank discrimination based least squares regression for image classification (Q6154781) (← links)
- Sparse Bayesian learning approach for discrete signal reconstruction (Q6157359) (← links)
- Multi-task sparse identification for closed-loop systems with general observation sequences (Q6157363) (← links)
- Riemannian thresholding methods for row-sparse and low-rank matrix recovery (Q6157448) (← links)
- Sequential image recovery using joint hierarchical Bayesian learning (Q6159245) (← links)
- Just least squares: binary compressive sampling with low generative intrinsic dimension (Q6159304) (← links)
- Smoothing accelerated proximal gradient method with fast convergence rate for nonsmooth convex optimization beyond differentiability (Q6161546) (← links)
- A non-convex piecewise quadratic approximation of \(\ell_0\) regularization: theory and accelerated algorithm (Q6162509) (← links)
- Enhanced total variation minimization for stable image reconstruction (Q6162738) (← links)
- A reduced half thresholding algorithm (Q6164988) (← links)
- Convergence of an asynchronous block-coordinate forward-backward algorithm for convex composite optimization (Q6166657) (← links)
- Data-driven reduced order models using invariant foliations, manifolds and autoencoders (Q6168858) (← links)
- First-order methods for convex optimization (Q6169988) (← links)
- Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation (Q6171683) (← links)
- Reconstruction of sparse recurrent connectivity and inputs from the nonlinear dynamics of neuronal networks (Q6172460) (← links)
- Nonoverlapping convex polytopes with vertices in a Boolean cube and other problems in coding theory (Q6173473) (← links)
- Lazy regular sensing (Q6175512) (← links)
- Sparsity-Inducing Nonconvex Nonseparable Regularization for Convex Image Processing (Q6176003) (← links)
- Entrywise limit theorems for eigenvectors of signal-plus-noise matrix models with weak signals (Q6178566) (← links)
- A Lorentzian-\(\ell_p\) norm regularization based algorithm for recovering sparse signals in two types of impulsive noise (Q6178626) (← links)
- Variational Bayesian inference for CP tensor completion with subspace information (Q6180798) (← links)
- Noisy linear inverse problems under convex constraints: exact risk asymptotics in high dimensions (Q6183752) (← links)
- Scaled proximal gradient methods for sparse optimization problems (Q6184263) (← links)
- Understanding Implicit Regularization in Over-Parameterized Single Index Model (Q6185498) (← links)
- Optimality conditions for Tucker low-rank tensor optimization (Q6188055) (← links)
- On the determination of Lagrange multipliers for a weighted Lasso problem using geometric and convex analysis techniques (Q6189680) (← links)
- Stable Recovery of Sparsely Corrupted Signals Through Justice Pursuit De-Noising (Q6191886) (← links)
- Dual graph-regularized sparse concept factorization for clustering (Q6195206) (← links)
- Representation recovery via \(L_1\)-norm minimization with corrupted data (Q6199567) (← links)
- The evaluation complexity of finding high-order minimizers of nonconvex optimization (Q6200212) (← links)
- A convergence analysis of hybrid gradient projection algorithm for constrained nonlinear equations with applications in compressed sensing (Q6200841) (← links)
- Dimension-free bounds for sums of dependent matrices and operators with heavy-tailed distributions (Q6200904) (← links)
- Sparks of symmetric matrices and their graphs (Q6202068) (← links)
- A Path-Based Approach to Constrained Sparse Optimization (Q6202768) (← links)
- Revisiting graph neural networks from hybrid regularized graph signal reconstruction (Q6488742) (← links)
- Sharp Bounds on the Approximation Rates, Metric Entropy, and n-Widths of Shallow Neural Networks (Q6489780) (← links)
- A combined method for time-varying parameter identification based on variational mode decomposition and generalized Morse wavelet (Q6490717) (← links)
- LPNN-based approach for LASSO problem via a sequence of regularized minimizations (Q6494692) (← links)
- Theoretical analysis of GOMP based on RIP and ROC (Q6498452) (← links)
- Reweighted covariance fitting based on nonconvex Schatten-\(p\) minimization for gridless direction of arrival estimation (Q6534404) (← links)
- Sparse Bayesian perspective for radar coincidence imaging with model errors (Q6534464) (← links)
- On randomized sampling Kaczmarz method with application in compressed sensing (Q6534553) (← links)
- Convergence properties of stochastic proximal subgradient method in solving a class of composite optimization problems with cardinality regularizer (Q6536946) (← links)
- Moving force identification based on group Lasso and compressed sensing (Q6538036) (← links)
- Towards probabilistic robust and sparsity-free compressive sampling in civil engineering: a review (Q6538371) (← links)
- First- and second-order optimality conditions of nonsmooth sparsity multiobjective optimization via variational analysis (Q6541380) (← links)
