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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 581–590 of 796 papers

TitleStatusHype
PAC-Bayesian matrix completion with a spectral scaled Student prior—0
Parametric Models for Mutual Kernel Matrix Completion—0
Partial Matrix Completion—0
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation—0
Penalty Decomposition Methods for Rank Minimization—0
Perturbation Analysis of Randomized SVD and its Applications to Statistics—0
Tracking Completion—0
Phase transitions and sample complexity in Bayes-optimal matrix factorization—0
Poisson Matrix Completion—0
Poisson Matrix Recovery and Completion—0
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