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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 176–200 of 796 papers

TitleStatusHype
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix CompletionCode0
Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR SamplingCode0
A Novel Plug-and-Play Approach for Adversarially Robust Generalization—0
A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery—0
Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensing—0
Adaptive and Implicit Regularization for Matrix CompletionCode1
Forecasting Algorithms for Causal Inference with Panel DataCode0
Propagation Map Reconstruction via Interpolation Assisted Matrix Completion—0
SP2: A Second Order Stochastic Polyak Method—0
A Perturbation Bound on the Subspace Estimator from Canonical ProjectionsCode0
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids—0
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network—0
MultiEarth 2022 -- The Champion Solution for the Matrix Completion Challenge via Multimodal Regression and Generation—0
Introducing the Huber mechanism for differentially private low-rank matrix completion—0
Robust Matrix Completion with Heavy-tailed Noise—0
A majorization-minimization algorithm for nonnegative binary matrix factorization—0
Quaternion Optimized Model with Sparse Regularization for Color Image Recovery—0
MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge—0
Survey of Matrix Completion Algorithms—0
Matrix Completion with Sparse Noisy Rows—0
Matrix Completion with Heterogonous Cost—0
Sensing Theorems for Unsupervised Learning in Linear Inverse ProblemsCode1
Perturbation Analysis of Randomized SVD and its Applications to Statistics—0
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference—0
Hierarchical Clustering and Matrix Completion for the Reconstruction of World Input-Output Tables—0
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