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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 341–350 of 796 papers

TitleStatusHype
Tensor Completion Made Practical—0
Robust Matrix Completion with Mixed Data Types—0
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
Low-rank matrix completion theory via Plucker coordinates—0
Ocean Reverberation Suppression via Matrix Completion with Sensor Failure—0
Orthogonal Inductive Matrix Completion—0
Nonconvex Matrix Completion with Linearly Parameterized Factors—0
Solving the Robust Matrix Completion Problem via a System of Nonlinear Equations—0
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