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Low-Rank Matrix Completion

Low-Rank Matrix Completion is an important problem with several applications in areas such as recommendation systems, sketching, and quantum tomography. The goal in matrix completion is to recover a low rank matrix, given a small number of entries of the matrix.

Source: Universal Matrix Completion

Papers

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Showing 61–70 of 158 papers

TitleStatusHype
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE—0
Simulation comparisons between Bayesian and de-biased estimators in low-rank matrix completionCode0
Structure-Preserving Progressive Low-rank Image Completion for Defending Adversarial Attacks—0
Exact Linear Convergence Rate Analysis for Low-Rank Symmetric Matrix Completion via Gradient Descent—0
Sparse Array Beamformer Design for Active and Passive Sensing—0
Mixed Membership Graph Clustering via Systematic Edge QueryCode0
Optimum Codesign for Image Denoising Between Type-2 Fuzzy Identifier and Matrix Completion Denoiser—0
Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method—0
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion—0
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold—0
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