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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 301–325 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
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