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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 301–310 of 796 papers

TitleStatusHype
Graph-Based Matrix Completion Applied to Weather Data—0
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images—0
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion—0
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids—0
Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction—0
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
Guaranteed Matrix Completion Under Multiple Linear Transformations—0
Guaranteed Matrix Completion via Non-convex Factorization—0
Fast Dual-Regularized Autoencoder for Sparse Biological Data—0
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