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Matrix Completion

Matrix Completion is a method for recovering lost information. It originates from machine learning and usually deals with highly sparse matrices. Missing or unknown data is estimated using the low-rank matrix of the known data.

Source: A Fast Matrix-Completion-Based Approach for Recommendation Systems

Papers

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Showing 276–300 of 796 papers

TitleStatusHype
Sharp Restricted Isometry Property Bounds for Low-rank Matrix Recovery Problems with Corrupted Measurements—0
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE—0
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure—0
Nonparametric Trace Regression in High Dimensions via Sign Series Representation—0
Implicit Regularization in Deep Tensor Factorization—0
Matrix completion based on Gaussian parameterized belief propagation—0
Multi-target prediction for dummies using two-branch neural networks—0
PAC-Bayesian Matrix Completion with a Spectral Scaled Student Prior—0
NoisyCUR: An algorithm for two-cost budgeted matrix completionCode0
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System—0
Adversarially-Trained Nonnegative Matrix FactorizationCode0
Simulation comparisons between Bayesian and de-biased estimators in low-rank matrix completionCode0
A Neural Network for SemigroupsCode0
Structure-Preserving Progressive Low-rank Image Completion for Defending Adversarial Attacks—0
Progresses and Challenges in Link Prediction—0
Implicit Regularization in Tensor FactorizationCode0
Forecasting Nonnegative Time Series via Sliding Mask Method (SMM) and Latent Clustered Forecast (LCF)—0
Policy Augmentation: An Exploration Strategy for Faster Convergence of Deep Reinforcement Learning AlgorithmsCode0
Wasserstein Graph Neural Networks for Graphs with Missing Attributes—0
Matrix Decomposition on Graphs: A Functional View—0
Exact Linear Convergence Rate Analysis for Low-Rank Symmetric Matrix Completion via Gradient Descent—0
Riemannian Perspective on Matrix Factorization—0
Unlabeled Principal Component Analysis and Matrix CompletionCode0
Sparse Array Beamformer Design for Active and Passive Sensing—0
Local Search Algorithms for Rank-Constrained Convex Optimization—0
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