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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 421–430 of 796 papers

TitleStatusHype
Inference and Uncertainty Quantification for Noisy Matrix Completion—0
Influenza Modeling Based on Massive Feature Engineering and International Flow Deconvolution—0
Information-theoretic Bounds on Matrix Completion under Union of Subspaces Model—0
Intelligent Reflecting Surface for Massive Device Connectivity: Joint Activity Detection and Channel Estimation—0
Interpretable Matrix Completion: A Discrete Optimization Approach—0
Introducing the Huber mechanism for differentially private low-rank matrix completion—0
Iterative missing value imputation based on feature importance—0
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System—0
k-Space Deep Learning for Reference-free EPI Ghost Correction—0
L_2,1-Norm Regularized Quaternion Matrix Completion Using Sparse Representation and Quaternion QR Decomposition—0
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