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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 301–310 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
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