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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
Robust Task Clustering for Deep Many-Task Learning—0
VIGAN: Missing View Imputation with Generative Adversarial NetworksCode0
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
Nearly Optimal Robust Matrix Completion—0
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
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