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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 611–620 of 796 papers

TitleStatusHype
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent—0
Provable Inductive Matrix Completion—0
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
Triple Simplex Matrix Completion for Expense Forecasting—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
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