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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 376–400 of 796 papers

TitleStatusHype
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
Hierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures—0
High Dimensional Factor Analysis with Weak Factors—0
High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization—0
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