ADMiRA: Atomic Decomposition for Minimum Rank Approximation
From MaRDI portal
Publication:5281301
DOI10.1109/TIT.2010.2054251zbMath1366.94112arXiv0905.0044MaRDI QIDQ5281301
Publication date: 27 July 2017
Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/0905.0044
Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Matrix completion problems (15A83)
Related Items (35)
Low-rank matrix recovery with Ky Fan 2-\(k\)-norm ⋮ Minimum \( n\)-rank approximation via iterative hard thresholding ⋮ Low rank matrix recovery from rank one measurements ⋮ Generalizing CoSaMP to signals from a union of low dimensional linear subspaces ⋮ Guarantees of Riemannian optimization for low rank matrix completion ⋮ Rank-constrained optimization and its applications ⋮ Penalty decomposition methods for rank minimization ⋮ An alternating minimization method for matrix completion problems ⋮ Exact minimum rank approximation via Schatten \(p\)-norm minimization ⋮ Implicit regularization in nonconvex statistical estimation: gradient descent converges linearly for phase retrieval, matrix completion, and blind deconvolution ⋮ CGIHT: conjugate gradient iterative hard thresholding for compressed sensing and matrix completion ⋮ Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm ⋮ Stable low-rank matrix recovery via null space properties ⋮ Uniqueness conditions for low-rank matrix recovery ⋮ Enhancing matrix completion using a modified second-order total variation ⋮ A relaxed interior point method for low-rank semidefinite programming problems with applications to matrix completion ⋮ Decomposition into low-rank plus additive matrices for background/foreground separation: a review for a comparative evaluation with a large-scale dataset ⋮ Matrix completion under interval uncertainty ⋮ Matrix recipes for hard thresholding methods ⋮ Convergence of projected Landweber iteration for matrix rank minimization ⋮ Convergence of fixed-point continuation algorithms for matrix rank minimization ⋮ Fixed-rank matrix factorizations and Riemannian low-rank optimization ⋮ A penalty decomposition method for rank minimization problem with affine constraints ⋮ Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach ⋮ Convergence analysis of projected gradient descent for Schatten-\(p\) nonconvex matrix recovery ⋮ Learning non-parametric basis independent models from point queries via low-rank methods ⋮ Guarantees of Riemannian Optimization for Low Rank Matrix Recovery ⋮ Low-rank dynamic mode decomposition: an exact and tractable solution ⋮ ADMiRA ⋮ Finding Low-Rank Solutions via Nonconvex Matrix Factorization, Efficiently and Provably ⋮ An adaptation for iterative structured matrix completion ⋮ Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion ⋮ A non-convex algorithm framework based on DC programming and DCA for matrix completion ⋮ Low-rank matrix completion via preconditioned optimization on the Grassmann manifold ⋮ Matrix Rigidity and the Ill-Posedness of Robust PCA and Matrix Completion
Uses Software
This page was built for publication: ADMiRA: Atomic Decomposition for Minimum Rank Approximation