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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 601–610 of 796 papers

TitleStatusHype
On the Predictability of Human Assessment: when Matrix Completion Meets NLP Evaluation—0
On the properties of variational approximations of Gibbs posteriors—0
On the simplicity and conditioning of low rank semidefinite programs—0
On the Robustness of Cross-Concentrated Sampling for Matrix Completion—0
Optimal (0,1)-Matrix Completion with Majorization Ordered Objectives (To the memory of Pravin Varaiya)—0
Optimal Exact Matrix Completion Under new Parametrization—0
Optimal Algorithms for Latent Bandits with Cluster Structure—0
Disjunctive Branch-And-Bound for Certifiably Optimal Low-Rank Matrix Completion—0
Optimal Low-Rank Tensor Recovery from Separable Measurements: Four Contractions Suffice—0
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion—0
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