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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
Optimal Algorithms for Latent Bandits with Cluster Structure—0
Quantizing Heavy-tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery—0
Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization—0
Generalization Bounds for Inductive Matrix Completion in Low-noise Settings—0
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power InjectionsCode0
Learning Transition Operators From Sparse Space-Time Samples—0
Doubly robust nearest neighbors in factor models—0
Efficient Rigid Body Localization based on Euclidean Distance Matrix Completion for AGV Positioning under Harsh Environment—0
Low Rank Quaternion Matrix Completion Based on Quaternion QR Decomposition and Sparse Regularizer—0
Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete DataCode0
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