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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 51–60 of 796 papers

TitleStatusHype
Advancing Matrix Completion by Modeling Extra Structures beyond Low-Rankness—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
Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion—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
A Fast Matrix-Completion-Based Approach for Recommendation Systems—0
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion—0
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms—0
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