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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 451–500 of 796 papers

TitleStatusHype
Leveraged Matrix Completion with Noise—0
Matrix Completion with Noisy Entries and Outliers—0
Matrix Completion with Noisy Side Information—0
Matrix Completion with Nonconvex Regularization: Spectral Operators and Scalable Algorithms—0
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM—0
Matrix completion with queries—0
Matrix Completion With Selective Sampling—0
Matrix Completion with Sparse Noisy Rows—0
Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference—0
Matrix Completion with Weighted Constraint for Haplotype Estimation—0
Matrix Data Deep Decoder - Geometric Learning for Structured Data Completion—0
Matrix Decomposition on Graphs: A Functional View—0
Matrix Estimation for Offline Reinforcement Learning with Low-Rank Structure—0
Matrix Factorization via Deep Learning—0
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms—0
Targeted matrix completion—0
Spectral Perturbation Meets Incomplete Multi-view Data—0
Temporal Matrix Completion with Locally Linear Latent Factors for Medical Applications—0
Maximum Entropy Kernels for System Identification—0
Max-Norm Optimization for Robust Matrix Recovery—0
MC2G: An Efficient Algorithm for Matrix Completion with Social and Item Similarity Graphs—0
Median Matrix Completion: from Embarrassment to Optimality—0
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA—0
Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model—0
Tensor Completion Made Practical—0
Minimax Lower Bounds for Noisy Matrix Completion Under Sparse Factor Models—0
Misclassification excess risk bounds for 1-bit matrix completion—0
MISNN: Multiple Imputation via Semi-parametric Neural Networks—0
Missing Entries Matrix Approximation and Completion—0
Tensor graph convolutional neural network—0
Mistake Bounds for Binary Matrix Completion—0
Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness—0
Tensor Methods for Nonlinear Matrix Completion—0
Mixture Matrix Completion—0
Mixture Matrix Completion—0
Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes—0
Multi-Channel Hypergraph Contrastive Learning for Matrix Completion—0
MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge—0
MultiEarth 2022 -- The Champion Solution for the Matrix Completion Challenge via Multimodal Regression and Generation—0
Multispectral snapshot demosaicing via non-convex matrix completion—0
Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion—0
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion—0
Multiple Testing of Linear Forms for Noisy Matrix Completion—0
Multi-Target Prediction: A Unifying View on Problems and Methods—0
Multi-View Matrix Completion for Multi-Label Image Classification—0
Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization—0
Mutual Kernel Matrix Completion—0
N^2: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion—0
Nearly-optimal Robust Matrix Completion—0
Nearly Optimal Robust Matrix Completion—0
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