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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 501–550 of 796 papers

TitleStatusHype
Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach—0
Matrix Completion with Heterogonous Cost—0
Matrix Completion with Hierarchical Graph Side Information—0
Matrix Completion with Hypergraphs:Sharp Thresholds and Efficient Algorithms—0
Matrix Completion with Model-free Weighting—0
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
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
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
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
Mistake Bounds for Binary Matrix Completion—0
Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness—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
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
Negative Binomial Matrix Completion—0
Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations—0
Network Inference by Learned Node-Specific Degree Prior—0
New Hardness Results for Low-Rank Matrix Completion—0
New Perspectives on k-Support and Cluster Norms—0
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