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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 241–250 of 796 papers

TitleStatusHype
Estimation of Missing Data in Intelligent Transportation System—0
Euclidean Distance Matrix Completion via Asymmetric Projected Gradient Descent—0
Deep Linear Networks for Matrix Completion -- An Infinite Depth Limit—0
Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-rank Matrix Completion—0
Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data—0
Exact tensor completion with sum-of-squares—0
Multi-target prediction for dummies using two-branch neural networks—0
Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints—0
Exponential Family Matrix Completion under Structural Constraints—0
Deep Learning Approach for Matrix Completion Using Manifold Learning—0
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