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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 681–690 of 796 papers

TitleStatusHype
Universal Matrix Completion—0
Scalable Recommender Systems through Recursive Evidence Chains—0
Scalable Recommender Systemsthrough Recursive Evidence Chains—0
Scaled Gradients on Grassmann Manifolds for Matrix Completion—0
Scaled stochastic gradient descent for low-rank matrix completion—0
Secrets of Matrix Factorization: Approximations, Numerics, Manifold Optimization and Random Restarts—0
Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions—0
Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning—0
Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensing—0
Sensor Network Localization via Riemannian Conjugate Gradient and Rank Reduction: An Extended Version—0
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