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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 731–740 of 796 papers

TitleStatusHype
CUR Algorithm with Incomplete Matrix Observation—0
New Perspectives on k-Support and Cluster Norms—0
Computational Limits for Matrix Completion—0
Universal Matrix Completion—0
Phase transitions and sample complexity in Bayes-optimal matrix factorization—0
Low-Rank Modeling and Its Applications in Image Analysis—0
Online Matrix Completion Through Nuclear Norm Regularisation—0
Understanding Alternating Minimization for Matrix Completion—0
A Novel Two-Step Method for Cross Language Representation Learning—0
Matrix Completion From any Given Set of Observations—0
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