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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 31–40 of 796 papers

TitleStatusHype
k-Space Deep Learning for Accelerated MRICode1
Graph Convolutional Matrix CompletionCode1
Geometric Matrix Completion with Recurrent Multi-Graph Neural NetworksCode1
Matrix Completion and Low-Rank SVD via Fast Alternating Least SquaresCode1
Generalized Low Rank ModelsCode1
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 Completion—0
Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models—0
Optimal Transport with Heterogeneously Missing Data—0
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