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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 676–700 of 796 papers

TitleStatusHype
Representational Transfer Learning for Matrix Completion—0
Representation learning of drug and disease terms for drug repositioning—0
RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees—0
Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees—0
Riemannian Perspective on Matrix Factorization—0
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold—0
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis—0
Robust Egoistic Rigid Body Localization—0
Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method—0
Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer—0
Spectral Geometric Matrix CompletionCode0
Provable Low Rank Phase RetrievalCode0
Collaborative Filtering with Graph Information: Consistency and Scalable MethodsCode0
Modeling longitudinal data using matrix completionCode0
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender SystemsCode0
Algebraic Variety Models for High-Rank Matrix CompletionCode0
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral FiltersCode0
Deep Models of Interactions Across SetsCode0
GNMR: A provable one-line algorithm for low rank matrix recoveryCode0
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19Code0
NGS Based Haplotype Assembly Using Matrix CompletionCode0
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual PredictionCode0
Policy Augmentation: An Exploration Strategy for Faster Convergence of Deep Reinforcement Learning AlgorithmsCode0
Low-Rank Hankel Tensor Completion for Traffic Speed EstimationCode0
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