Pages that link to "Item:Q2896066"

The following pages link to Analysis of multi-stage convex relaxation for sparse regularization (Q2896066):

Displaying 50 items.

• Delete or merge regressors for linear model selection (Q78545) (← links)
• An alternating direction method with continuation for nonconvex low rank minimization (Q257130) (← links)
• Best subset selection via a modern optimization lens (Q282479) (← links)
• Oracle inequalities for the Lasso in the high-dimensional Aalen multiplicative intensity model (Q297474) (← links)
• Optimal computational and statistical rates of convergence for sparse nonconvex learning problems (Q482875) (← links)
• Two-stage convex relaxation approach to low-rank and sparsity regularized least squares loss (Q683720) (← links)
• Nonconcave penalized composite conditional likelihood estimation of sparse Ising models (Q693730) (← links)
• Nonconvex nonsmooth low-rank minimization for generalized image compressed sensing via group sparse representation (Q776102) (← links)
• The \(\ell_{2,q}\) regularized group sparse optimization: lower bound theory, recovery bound and algorithms (Q778013) (← links)
• A theoretical understanding of self-paced learning (Q778415) (← links)
• Efficient nonconvex sparse group feature selection via continuous and discrete optimization (Q892230) (← links)
• Relaxed sparse eigenvalue conditions for sparse estimation via non-convex regularized regression (Q1677029) (← links)
• \(\ell _p\) regularized low-rank approximation via iterative reweighted singular value minimization (Q1694395) (← links)
• A new nonconvex approach to low-rank matrix completion with application to image inpainting (Q1710943) (← links)
• A new piecewise quadratic approximation approach for \(L_0\) norm minimization problem (Q1729942) (← links)
• Structured nonconvex and nonsmooth optimization: algorithms and iteration complexity analysis (Q1734769) (← links)
• I-LAMM for sparse learning: simultaneous control of algorithmic complexity and statistical error (Q1750288) (← links)
• Capped \(\ell_p\) approximations for the composite \(\ell_0\) regularization problem (Q1785036) (← links)
• High-dimensional additive hazards models and the lasso (Q1950827) (← links)
• A novel robust principal component analysis algorithm of nonconvex rank approximation (Q2004251) (← links)
• A support-denoiser-driven framework for single image restoration (Q2020555) (← links)
• Stochastic nonlocal damage analysis by a machine learning approach (Q2020969) (← links)
• Linear convergence of inexact descent method and inexact proximal gradient algorithms for lower-order regularization problems (Q2022292) (← links)
• Tractable ADMM schemes for computing KKT points and local minimizers for \(\ell_0\)-minimization problems (Q2026765) (← links)
• A unified primal dual active set algorithm for nonconvex sparse recovery (Q2038299) (← links)
• Iteratively reweighted \(\ell_1\)-penalized robust regression (Q2044416) (← links)
• Inexact stochastic subgradient projection method for stochastic equilibrium problems with nonmonotone bifunctions: application to expected risk minimization in machine learning (Q2045021) (← links)
• Correntropy-based metric for robust twin support vector machine (Q2054026) (← links)
• Sparse classification: a scalable discrete optimization perspective (Q2071494) (← links)
• Analysis of generalized Bregman surrogate algorithms for nonsmooth nonconvex statistical learning (Q2073715) (← links)
• Weighted thresholding homotopy method for sparsity constrained optimization (Q2082209) (← links)
• Variable selection in convex quantile regression: \(\mathcal{L}_1\)-norm or \(\mathcal{L}_0\)-norm regularization? (Q2083962) (← links)
• A convex relaxation framework consisting of a primal-dual alternative algorithm for solving \(\ell_0\) sparsity-induced optimization problems with application to signal recovery based image restoration (Q2095175) (← links)
• Penalized wavelet estimation and robust denoising for irregular spaced data (Q2095705) (← links)
• A data-driven line search rule for support recovery in high-dimensional data analysis (Q2157522) (← links)
• A unifying framework of high-dimensional sparse estimation with difference-of-convex (DC) regularizations (Q2163076) (← links)
• Nonconvex regularization for sparse neural networks (Q2168678) (← links)
• The springback penalty for robust signal recovery (Q2168687) (← links)
• Separating variables to accelerate non-convex regularized optimization (Q2181546) (← links)
• Robust capped L1-norm twin support vector machine (Q2183604) (← links)
• Transformed \(\ell_1\) regularization for learning sparse deep neural networks (Q2185659) (← links)
• Sparse signal reconstruction via the approximations of \(\ell_0\) quasinorm (Q2190319) (← links)
• A class of null space conditions for sparse recovery via nonconvex, non-separable minimizations (Q2211058) (← links)
• A multi-stage convex relaxation approach to noisy structured low-rank matrix recovery (Q2220914) (← links)
• Minimizing a sum of clipped convex functions (Q2228412) (← links)
• On the superiority of PGMs to PDCAs in nonsmooth nonconvex sparse regression (Q2230800) (← links)
• An efficient non-convex total variation approach for image deblurring and denoising (Q2242083) (← links)
• Fixed point quasiconvex subgradient method (Q2282530) (← links)
• Sorted concave penalized regression (Q2284364) (← links)
• On integer and MPCC representability of affine sparsity (Q2294300) (← links)