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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 331–340 of 796 papers

TitleStatusHype
Robust Matrix Completion with Mixed Data Types—0
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient DescentCode1
Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)Code1
Implicit Regularization in Deep Learning May Not Be Explainable by NormsCode0
Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data—0
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold—0
Ocean Reverberation Suppression via Matrix Completion with Sensor Failure—0
Low-rank matrix completion theory via Plucker coordinates—0
Orthogonal Inductive Matrix Completion—0
Nonconvex Matrix Completion with Linearly Parameterized Factors—0
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