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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 301–325 of 796 papers

TitleStatusHype
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
Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion—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
Coherence and sufficient sampling densities for reconstruction in compressed sensing—0
Guaranteed Matrix Completion Under Multiple Linear Transformations—0
Guaranteed Matrix Completion via Non-convex Factorization—0
Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects—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
High-dimensional Time Series Prediction with Missing Values—0
High-Rank Matrix Completion and Clustering under Self-Expressive Models—0
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size—0
A note on the statistical view of matrix completion—0
Implicit bias of SGD in L_2-regularized linear DNNs: One-way jumps from high to low rank—0
Identifying global optimality for dictionary learning—0
Fast Dual-Regularized Autoencoder for Sparse Biological Data—0
IHT-Inspired Neural Network for Single-Snapshot DOA Estimation with Sparse Linear Arrays—0
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev—0
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