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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 661–670 of 796 papers

TitleStatusHype
Matrix Completion for Resolving Label Ambiguity—0
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm—0
Smooth PARAFAC Decomposition for Tensor Completion—0
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation—0
Optimal Low-Rank Tensor Recovery from Separable Measurements: Four Contractions Suffice—0
Prediction and Quantification of Individual Athletic Performance—0
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
Social Trust Prediction via Max-norm Constrained 1-bit Matrix Completion—0
Regularization-free estimation in trace regression with symmetric positive semidefinite matrices—0
Poisson Matrix Recovery and Completion—0
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