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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 301–350 of 796 papers

TitleStatusHype
Adversarial Crowdsourcing Through Robust Rank-One Matrix CompletionCode1
Optimum Codesign for Image Denoising Between Type-2 Fuzzy Identifier and Matrix Completion Denoiser—0
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev—0
Geometric Matrix Completion: A Functional ViewCode0
Crosslingual Topic Modeling with WikiPDACode1
DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19Code0
Recent Developments on Factor Models and its Applications in Econometric Learning—0
Meta-learning based Alternating Minimization Algorithm for Non-convex OptimizationCode0
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order MethodCode1
Efficient Model-Based Collaborative Filtering with Fast Adaptive PCACode0
Clustering of Nonnegative Data and an Application to Matrix Completion—0
Low-rank matrix recovery with non-quadratic loss: projected gradient method and regularity projection oracle—0
Column _2,0-norm regularized factorization model of low-rank matrix recovery and its computation—0
Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method—0
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion—0
Conservative Stochastic Optimization with Expectation Constraints—0
Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion—0
Autoencoder-based Graph Construction for Semi-supervised Learning—0
A regularized deep matrix factorized model of matrix completion for image restorationCode0
Compressed sensing of low-rank plus sparse matricesCode1
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering ApproachCode1
Partial Trace Regression and Low-Rank Kraus DecompositionCode0
Fixing Inventory Inaccuracies At Scale—0
Short-Term Traffic Forecasting Using High-Resolution Traffic Data—0
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian CopulaCode1
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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