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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 471–480 of 796 papers

TitleStatusHype
Matrix Co-completion for Multi-label Classification with Missing Features and Labels—0
Matrix Coherence and the Nystrom Method—0
Matrix completion and extrapolation via kernel regression—0
Matrix Completion and Performance Guarantees for Single Individual Haplotyping—0
Matrix Completion and Related Problems via Strong Duality—0
Matrix completion based on Gaussian parameterized belief propagation—0
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data—0
Matrix Completion for Resolving Label Ambiguity—0
Matrix Completion for Structured Observations—0
Matrix Completion From any Given Set of Observations—0
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