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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 31–40 of 796 papers

Title	Date	Tasks	Status	Hype	Score
Sensing Theorems for Unsupervised Learning in Linear Inverse Problems	Mar 23, 2022	Dictionary LearningMatrix Completion	CodeCode Available	1	5
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark	Nov 25, 2021	Matrix CompletionMoving Object Detection	CodeCode Available	1	5
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method	Sep 7, 2020	Low-Rank Matrix CompletionMatrix Completion	CodeCode Available	1	5
Generalized Low Rank Models	Oct 1, 2014	ClusteringDenoising	CodeCode Available	1	5
Graph Convolutional Matrix Completion	Jun 7, 2017	Collaborative FilteringLink Prediction	CodeCode Available	1	5
Deep Collective Matrix Factorization for Augmented Multi-View Learning	Nov 28, 2018	Bayesian OptimizationMatrix Completion	CodeCode Available	0	5
Spectral Geometric Matrix Completion	Nov 17, 2019	Matrix CompletionRecommendation Systems	CodeCode Available	0	5
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation	Jun 12, 2025	Contrastive LearningDenoising	CodeCode Available	0	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	0	5
Deep Models of Interactions Across Sets	Mar 7, 2018	Collaborative FilteringMatrix Completion	CodeCode Available	0	5

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