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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 461–470 of 796 papers

TitleStatusHype
Low-Rank Modeling and Its Applications in Image Analysis—0
Low-rank optimization for distance matrix completion—0
Low-rank optimization with trace norm penalty—0
Low Rank Quaternion Matrix Completion Based on Quaternion QR Decomposition and Sparse Regularizer—0
Low Rank Quaternion Matrix Recovery via Logarithmic Approximation—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
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