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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 41–50 of 796 papers

Title	Date	Tasks	Status	Score
Adversarially-Trained Nonnegative Matrix Factorization	Apr 10, 2021	Dimensionality ReductionMatrix Completion	CodeCode Available	5
Graphon Estimation from Partially Observed Network Data	Jun 2, 2019	Graphon EstimationMatrix Completion	CodeCode Available	5
Deep Collective Matrix Factorization for Augmented Multi-View Learning	Nov 28, 2018	Bayesian OptimizationMatrix Completion	CodeCode Available	5
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion	Aug 24, 2022	Collaborative FilteringMatrix Completion	CodeCode Available	5
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset	Nov 4, 2015	Matrix Completion	CodeCode Available	5
Spectral Geometric Matrix Completion	Nov 17, 2019	Matrix CompletionRecommendation Systems	CodeCode Available	5
Deep Models of Interactions Across Sets	Mar 7, 2018	Collaborative FilteringMatrix Completion	CodeCode Available	5
Doubly robust nearest neighbors in factor models	Nov 25, 2022	counterfactualCounterfactual Inference	CodeCode Available	5
Collective Matrix Completion	Jul 24, 2018	Matrix Completion	CodeCode Available	5
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters	May 22, 2017	Community DetectionDeep Learning	CodeCode Available	5

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