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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 621–630 of 796 papers

TitleStatusHype
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
R3MC: A Riemannian three-factor algorithm for low-rank matrix completion—0
Scalable and Robust Community Detection with Randomized Sketching—0
Matrices with Gaussian noise: optimal estimates for singular subspace perturbation—0
Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization—0
Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion—0
Ranking with Features: Algorithm and A Graph Theoretic Analysis—0
Recent Developments on Factor Models and its Applications in Econometric Learning—0
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