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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 351–400 of 796 papers

TitleStatusHype
Extendable Neural Matrix Completion—0
Extended Gauss-Newton and ADMM-Gauss-Newton Algorithms for Low-Rank Matrix Optimization—0
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery—0
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods—0
Factorizing LambdaMART for cold start recommendations—0
Fast Algorithms for Robust PCA via Gradient Descent—0
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev—0
Fast Dual-Regularized Autoencoder for Sparse Biological Data—0
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size—0
Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion—0
Fast Exact Matrix Completion with Finite Samples—0
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees—0
Fast matrix completion without the condition number—0
Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation—0
Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models—0
Fast Optimization Algorithm on Riemannian Manifolds and Its Application in Low-Rank Representation—0
Fast Two-photon Microscopy by Neuroimaging with Oblong Random Acquisition (NORA)—0
Fine-grained Generalization Analysis of Inductive Matrix Completion—0
Results on the algebraic matroid of the determinantal variety—0
Fitting Spectral Decay with the k-Support Norm—0
Fixed-rank matrix factorizations and Riemannian low-rank optimization—0
Flat minima generalize for low-rank matrix recovery—0
Flexible Low-Rank Statistical Modeling with Side Information—0
Forecasting Nonnegative Time Series via Sliding Mask Method (SMM) and Latent Clustered Forecast (LCF)—0
Functional correspondence by matrix completion—0
Fundamental Conditions for Low-CP-Rank Tensor Completion—0
Fusion Subspace Clustering: Full and Incomplete Data—0
Generalization Bounds for Inductive Matrix Completion in Low-noise Settings—0
Generalization error bounds for kernel matrix completion and extrapolation—0
Generalized Conditional Gradient for Sparse Estimation—0
Generalized Low-Rank Matrix Completion Model with Overlapping Group Error Representation—0
Geometric Inference for General High-Dimensional Linear Inverse Problems—0
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network—0
Geometric Matrix Completion with Deep Conditional Random Fields—0
Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction—0
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization—0
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems—0
Going off the Grid: Iterative Model Selection for Biclustered Matrix Completion—0
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers—0
Graph-Based Matrix Completion Applied to Weather Data—0
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images—0
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids—0
Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction—0
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift—0
Guaranteed Matrix Completion Under Multiple Linear Transformations—0
Guaranteed Matrix Completion via Non-convex Factorization—0
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension—0
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion—0
Hierarchical Clustering and Matrix Completion for the Reconstruction of World Input-Output Tables—0
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