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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 1–10 of 796 papers

TitleStatusHype
New Hardness Results for Low-Rank Matrix Completion—0
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
N^2: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix CompletionCode0
Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models—0
Optimal Transport with Heterogeneously Missing Data—0
RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees—0
Adaptively-weighted Nearest Neighbors for Matrix CompletionCode0
Euclidean Distance Matrix Completion via Asymmetric Projected Gradient Descent—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
Truncated Matrix Completion - An Empirical Study—0
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