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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 351–360 of 796 papers

TitleStatusHype
Dropout: Explicit Forms and Capacity Control—0
On the simplicity and conditioning of low rank semidefinite programs—0
Online high rank matrix completion—0
Results on the algebraic matroid of the determinantal variety—0
On Robust Mean Estimation under Coordinate-level Corruption—0
Optimal Exact Matrix Completion Under new Parametrization—0
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems—0
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion—0
Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning—0
Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks—0
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