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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 751–760 of 796 papers

TitleStatusHype
Matrix Completion from a Few EntriesCode0
Bayesian Matrix Completion via Adaptive Relaxed Spectral RegularizationCode0
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm AssumptionCode0
Inductive Matrix Completion: No Bad Local Minima and a Fast AlgorithmCode0
Bayesian graph convolutional neural networks for semi-supervised classificationCode0
WARPd: A linearly convergent first-order method for inverse problems with approximate sharpness conditionsCode0
Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical PropertiesCode0
Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel FactorizationCode0
Matrix Completion from Noisy EntriesCode0
Mixed Membership Graph Clustering via Systematic Edge QueryCode0
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