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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 401–410 of 796 papers

TitleStatusHype
Low Rank Quaternion Matrix Recovery via Logarithmic Approximation—0
Widely Separated MIMO Radar Using Matrix Completion—0
Low-tubal-rank Tensor Completion using Alternating Minimization—0
LRSVRG-IMC: An SVRG-Based Algorithm for LowRank Inductive Matrix Completion—0
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion—0
Machine Learning Methods Economists Should Know About—0
Manopt, a Matlab toolbox for optimization on manifolds—0
Matrix Co-completion for Multi-label Classification with Missing Features and Labels—0
Matrix Coherence and the Nystrom Method—0
Matrix completion and extrapolation via kernel regression—0
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