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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 21–30 of 796 papers

TitleStatusHype
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order MethodCode1
Compressed sensing of low-rank plus sparse matricesCode1
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering ApproachCode1
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian CopulaCode1
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient DescentCode1
Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)Code1
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Inductive Matrix Completion Based on Graph Neural NetworksCode1
SAVOIAS: A Diverse, Multi-Category Visual Complexity DatasetCode1
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
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