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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 531–540 of 796 papers

TitleStatusHype
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
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