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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 761–770 of 796 papers

TitleStatusHype
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes FlowCode0
Provable Subspace Tracking from Missing Data and Matrix CompletionCode0
Interference-Aware Edge Runtime Prediction with Conformal Matrix CompletionCode0
Robust Matrix Completion for Discrete Rating-Scale DataCode0
Faster Matrix Completion Using Randomized SVDCode0
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex MinimizationCode0
Structured Learning of Compositional Sequential InterventionsCode0
Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine LearningCode0
Simulation comparisons between Bayesian and de-biased estimators in low-rank matrix completionCode0
A regularized deep matrix factorized model of matrix completion for image restorationCode0
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