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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 421–430 of 796 papers

TitleStatusHype
Nonconvex Rectangular Matrix Completion via Gradient Descent without _2, Regularization—0
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion—0
Imputation and low-rank estimation with Missing Not At Random dataCode0
Matrix Completion under Low-Rank Missing Mechanism—0
Interpretable Matrix Completion: A Discrete Optimization Approach—0
Matrix Factorization via Deep Learning—0
Mixture Matrix Completion—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
Bayesian graph convolutional neural networks for semi-supervised classificationCode0
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