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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

Showing 6170 of 796 papers

TitleStatusHype
DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19Code0
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
Spectral Geometric Matrix CompletionCode0
Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient RegularizationCode0
Counterfactual inference for sequential experimentsCode0
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
Algebraic Variety Models for High-Rank Matrix CompletionCode0
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power InjectionsCode0
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale DatasetCode0
Collaborative Filtering with Graph Information: Consistency and Scalable MethodsCode0
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