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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 281–290 of 796 papers

TitleStatusHype
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
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