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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 471–480 of 796 papers

TitleStatusHype
MC2G: An Efficient Algorithm for Matrix Completion with Social and Item Similarity Graphs—0
Median Matrix Completion: from Embarrassment to Optimality—0
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA—0
Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model—0
Tensor Completion Made Practical—0
Minimax Lower Bounds for Noisy Matrix Completion Under Sparse Factor Models—0
Misclassification excess risk bounds for 1-bit matrix completion—0
MISNN: Multiple Imputation via Semi-parametric Neural Networks—0
Missing Entries Matrix Approximation and Completion—0
Tensor graph convolutional neural network—0
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