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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 641–650 of 796 papers

TitleStatusHype
Provable Low Rank Plus Sparse Matrix Separation Via Nonconvex Regularizers—0
Provable Non-linear Inductive Matrix Completion—0
Provable Tensor-Train Format Tensor Completion by Riemannian Optimization—0
Proximal Riemannian Pursuit for Large-Scale Trace-Norm Minimization—0
Pseudo-Bayesian Robust PCA: Algorithms and Analyses—0
PU Learning for Matrix Completion—0
Quantized Matrix Completion for Personalized Learning—0
Quantizing Heavy-tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery—0
Quaternion Matrix Completion Using Untrained Quaternion Convolutional Neural Network for Color Image Inpainting—0
Quaternion Optimized Model with Sparse Regularization for Color Image Recovery—0
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