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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 426–450 of 796 papers

TitleStatusHype
Geometric Matrix Completion with Deep Conditional Random Fields—0
Computing large market equilibria using abstractions—0
Nonconvex Rectangular Matrix Completion via Gradient Descent without _2, Regularization—0
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion—0
Imputation and low-rank estimation with Missing Not At Random dataCode0
Matrix Completion under Low-Rank Missing Mechanism—0
Interpretable Matrix Completion: A Discrete Optimization Approach—0
Matrix Factorization via Deep Learning—0
Mixture Matrix Completion—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
Bayesian graph convolutional neural networks for semi-supervised classificationCode0
Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference—0
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
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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