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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 181–190 of 796 papers

TitleStatusHype
A Unified Framework for Structured Low-rank Matrix Learning—0
DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed With Deep Features—0
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE—0
Deep Learning Approach for Matrix Completion Using Manifold Learning—0
Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data—0
Deep Linear Networks for Matrix Completion -- An Infinite Depth Limit—0
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery—0
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion—0
Deep Non-Rigid Structure from Motion with Missing Data—0
Color Image Recovery Using Generalized Matrix Completion over Higher-Order Finite Dimensional Algebra—0
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