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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
Communication-Efficient Projection-Free Algorithm for Distributed Optimization—0
Transduction with Matrix Completion Using Smoothed Rank Function—0
Projection-Free Bandit Convex Optimization—0
Extendable Neural Matrix Completion—0
Sparse Group Inductive Matrix Completion—0
Tensor Methods for Nonlinear Matrix Completion—0
Parametric Models for Mutual Kernel Matrix Completion—0
Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-rank Matrix Completion—0
Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion—0
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