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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 91–100 of 796 papers

TitleStatusHype
Autoencoder-based Graph Construction for Semi-supervised Learning—0
Multi-target prediction for dummies using two-branch neural networks—0
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion—0
Background Subtraction via Fast Robust Matrix Completion—0
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
A Riemannian gossip approach to decentralized matrix completion—0
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