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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 771–780 of 796 papers

Title	Date	Tasks	Status
Fast Low-Rank Matrix Learning with Nonconvex Regularization	Dec 3, 2015	Matrix Completion	CodeCode Available
A Neural Network for Semigroups	Mar 12, 2021	DenoisingImage Reconstruction	CodeCode Available
Matrix Completion in the Unit Hypercube via Structured Matrix Factorization	May 30, 2019	Matrix Completion	CodeCode Available
Unlabeled Principal Component Analysis and Matrix Completion	Jan 23, 2021	Matrix Completion	CodeCode Available
Matrix Completion on Graphs	Aug 7, 2014	Collaborative FilteringMatrix Completion	CodeCode Available
Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation	Jan 8, 2016	Collaborative FilteringMatrix Completion	CodeCode Available
Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data	Nov 23, 2022	ImputationMatrix Completion	CodeCode Available
Adaptive Matrix Completion for the Users and the Items in Tail	Apr 22, 2019	Collaborative FilteringLow-Rank Matrix Completion	CodeCode Available
Robust PCA via Outlier Pursuit	Oct 20, 2010	Collaborative FilteringDimensionality Reduction	CodeCode Available
Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion	Apr 4, 2014	Low-Rank Matrix CompletionMatrix Completion	CodeCode Available

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