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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 301–310 of 796 papers

TitleStatusHype
Estimation of Missing Data in Intelligent Transportation System—0
A new accelerated gradient method inspired by continuous-time perspective—0
Matrix Data Deep Decoder - Geometric Learning for Structured Data Completion—0
Inductive Collaborative Filtering via Relation Graph Learning—0
Outlier-robust sparse/low-rank least-squares regression and robust matrix completionCode0
Deep Learning Approach for Matrix Completion Using Manifold Learning—0
A generalised log-determinant regularizer for online semi-definite programming and its applications—0
Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem—0
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices—0
Mixed Membership Graph Clustering via Systematic Edge QueryCode0
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