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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 741–750 of 796 papers

TitleStatusHype
A Unified Framework for Structured Low-rank Matrix Learning—0
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
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Smooth PARAFAC Decomposition for Tensor Completion—0
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