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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 211–220 of 796 papers

TitleStatusHype
Splitting numerical integration for matrix completion—0
Color Image Inpainting via Robust Pure Quaternion Matrix Completion: Error Bound and Weighted Loss—0
Inductive Matrix Completion: No Bad Local Minima and a Fast AlgorithmCode0
LRSVRG-IMC: An SVRG-Based Algorithm for LowRank Inductive Matrix Completion—0
Dense Air Quality Maps Using Regressive Facility Location Based Drive By Sensing—0
Variational Bayesian Filtering with Subspace Information for Extreme Spatio-Temporal Matrix Completion—0
Matrix Completion with Hierarchical Graph Side Information—0
On Asymptotic Linear Convergence of Projected Gradient Descent for Constrained Least Squares—0
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective—0
Fine-grained Generalization Analysis of Inductive Matrix Completion—0
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