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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 526–550 of 796 papers

TitleStatusHype
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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