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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 326–350 of 796 papers

TitleStatusHype
Median Matrix Completion: from Embarrassment to Optimality—0
Efficient MCMC Sampling for Bayesian Matrix Factorization by Breaking Posterior Symmetries—0
MC2G: An Efficient Algorithm for Matrix Completion with Social and Item Similarity Graphs—0
Discrete-Aware Matrix Completion via Proximal Gradient—0
Tensor Completion Made Practical—0
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
Solving the Robust Matrix Completion Problem via a System of Nonlinear Equations—0
Dropout: Explicit Forms and Capacity Control—0
On the simplicity and conditioning of low rank semidefinite programs—0
Online high rank matrix completion—0
Results on the algebraic matroid of the determinantal variety—0
On Robust Mean Estimation under Coordinate-level Corruption—0
Optimal Exact Matrix Completion Under new Parametrization—0
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems—0
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion—0
Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning—0
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