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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
On the convex geometry of blind deconvolution and matrix completion—0
Multispectral snapshot demosaicing via non-convex 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
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
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