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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 661–670 of 796 papers

TitleStatusHype
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders—0
Recovery guarantee of weighted low-rank approximation via alternating minimization—0
Recovery of damped exponentials using structured low rank matrix completion—0
Recovery of Piecewise Smooth Images from Few Fourier Samples—0
Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems—0
Reexamining Low Rank Matrix Factorization for Trace Norm Regularization—0
Reflection Removal Using Low-Rank Matrix Completion—0
Region-wise matching for image inpainting based on adaptive weighted low-rank decomposition—0
Regret Guarantees for Item-Item Collaborative Filtering—0
Regularization-free estimation in trace regression with symmetric positive semidefinite matrices—0
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