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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 701–710 of 796 papers

TitleStatusHype
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
Sequential Matrix Completion—0
A new accelerated gradient method inspired by continuous-time perspective—0
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm—0
A New Theory for Matrix Completion—0
Sharp Restricted Isometry Property Bounds for Low-rank Matrix Recovery Problems with Corrupted Measurements—0
Low-rank matrix completion theory via Plucker coordinates—0
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion—0
Short-Term Traffic Forecasting Using High-Resolution Traffic Data—0
Signal Recovery on Graphs: Variation Minimization—0
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