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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 651–700 of 796 papers

TitleStatusHype
R3MC: A Riemannian three-factor algorithm for low-rank matrix completion—0
Scalable and Robust Community Detection with Randomized Sketching—0
Matrices with Gaussian noise: optimal estimates for singular subspace perturbation—0
Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization—0
Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion—0
Ranking with Features: Algorithm and A Graph Theoretic Analysis—0
Recent Developments on Factor Models and its Applications in Econometric Learning—0
Recognizing retinal ganglion cells in the dark—0
Recommendations from Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization—0
Recommendation via matrix completion using Kolmogorov complexity—0
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders—0
Recovery guarantee of weighted low-rank approximation via alternating minimization—0
Recovery of damped exponentials using structured low rank matrix completion—0
Recovery of Piecewise Smooth Images from Few Fourier Samples—0
Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems—0
Reexamining Low Rank Matrix Factorization for Trace Norm Regularization—0
Reflection Removal Using Low-Rank Matrix Completion—0
Region-wise matching for image inpainting based on adaptive weighted low-rank decomposition—0
Regret Guarantees for Item-Item Collaborative Filtering—0
Regularization-free estimation in trace regression with symmetric positive semidefinite matrices—0
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning—0
Relax and Randomize : From Value to Algorithms—0
Relaxed Leverage Sampling for Low-rank Matrix Completion—0
Relevance Singular Vector Machine for low-rank matrix sensing—0
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion—0
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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