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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 261–270 of 796 papers

TitleStatusHype
Nonparametric Trace Regression in High Dimensions via Sign Series Representation—0
Implicit Regularization in Deep Tensor Factorization—0
Matrix completion based on Gaussian parameterized belief propagation—0
Multi-target prediction for dummies using two-branch neural networks—0
NoisyCUR: An algorithm for two-cost budgeted matrix completionCode0
PAC-Bayesian Matrix Completion with a Spectral Scaled Student Prior—0
Deep Permutation Equivariant Structure from MotionCode1
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System—0
Adversarially-Trained Nonnegative Matrix FactorizationCode0
Simulation comparisons between Bayesian and de-biased estimators in low-rank matrix completionCode0
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