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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 141–150 of 796 papers

TitleStatusHype
Triple Simplex Matrix Completion for Expense Forecasting—0
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion—0
Robust matrix completion via Novel M-estimator Functions—0
Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer—0
Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI—0
IHT-Inspired Neural Network for Single-Snapshot DOA Estimation with Sparse Linear Arrays—0
L_2,1-Norm Regularized Quaternion Matrix Completion Using Sparse Representation and Quaternion QR Decomposition—0
Widely Separated MIMO Radar Using Matrix Completion—0
Applications of Nature-Inspired Metaheuristic Algorithms for Tackling Optimization Problems Across DisciplinesCode0
Matrix Completion in Almost-Verification Time—0
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