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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 521–530 of 796 papers

TitleStatusHype
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers—0
Efficient Low Rank Tensor Ring Completion—0
Recommendation via matrix completion using Kolmogorov complexity—0
Reflection Removal Using Low-Rank Matrix Completion—0
Concentration of tempered posteriors and of their variational approximations—0
Reexamining Low Rank Matrix Factorization for Trace Norm Regularization—0
Crowdsourcing with Sparsely Interacting Workers—0
Graph Convolutional Matrix CompletionCode1
Empirical Bayes Matrix Completion—0
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral FiltersCode0
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