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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 431–440 of 796 papers

TitleStatusHype
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
Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference—0
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
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