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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 221–230 of 796 papers

TitleStatusHype
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction—0
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion—0
Background Subtraction via Fast Robust Matrix Completion—0
Efficiently escaping saddle points on manifolds—0
Efficient MCMC Sampling for Bayesian Matrix Factorization by Breaking Posterior Symmetries—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
Convergence of the majorized PAM method with subspace correction for low-rank composite factorization model—0
Deep Linear Networks for Matrix Completion -- An Infinite Depth Limit—0
Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data—0
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