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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 521–530 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
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
Mistake Bounds for Binary Matrix Completion—0
Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness—0
Mixture Matrix Completion—0
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