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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 181–190 of 796 papers

TitleStatusHype
Bayesian Learning for Low-Rank matrix reconstruction—0
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective—0
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion—0
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