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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 411–420 of 796 papers

TitleStatusHype
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction—0
Provable Tensor Ring CompletionCode0
Matrix Completion via Nonconvex Regularization: Convergence of the Proximal Gradient AlgorithmCode0
Multispectral snapshot demosaicing via non-convex matrix completion—0
On the convex geometry of blind deconvolution and matrix completion—0
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization—0
Provable Low Rank Phase RetrievalCode0
Robust Matrix Completion State Estimation in Distribution Systems—0
Geometric Matrix Completion with Deep Conditional Random Fields—0
Computing large market equilibria using abstractions—0
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