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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 51–75 of 158 papers

TitleStatusHype
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction—0
Uncertainty Quantification For Low-Rank Matrix Completion With Heterogeneous and Sub-Exponential Noise—0
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders—0
Weighted Low Rank Matrix Approximation and Acceleration—0
GNMR: A provable one-line algorithm for low rank matrix recoveryCode0
Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few SamplesCode1
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
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order MethodCode1
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
Low-rank matrix completion theory via Plucker coordinates—0
Bounded Manifold Completion—0
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery—0
Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine LearningCode0
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion—0
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