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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 441–450 of 796 papers

TitleStatusHype
Scalable Recommender Systemsthrough Recursive Evidence Chains—0
Faster Matrix Completion Using Randomized SVDCode0
Provable Subspace Tracking from Missing Data and Matrix CompletionCode0
Training Complex Models with Multi-Task Weak SupervisionCode0
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview—0
Modeling longitudinal data using matrix completionCode0
Matrix Completion with Weighted Constraint for Haplotype Estimation—0
Clipped Matrix Completion: A Remedy for Ceiling Effects—0
Multi-Target Prediction: A Unifying View on Problems and Methods—0
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine LearningCode0
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