Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/4105
Title: Semi-supervised affinity matrix learning via dual-channel information recovery
Author(s): Liu, Hui 
Author(s): Jia, Y.
Hou, J.
Kwong, S.
Zhang, Q.
Issue Date: 2021
Publisher: IEEE
Journal: IEEE Transactions on Cybernetics 
Volume: 52
Issue: 8
Start page: 7919
End page: 7930
Abstract: 
This article explores the problem of semisupervised affinity matrix learning, that is, learning an affinity matrix of data samples under the supervision of a small number of pairwise constraints (PCs). By observing that both the matrix encoding PCs, called pairwise constraint matrix (PCM) and the empirically constructed affinity matrix (EAM), express the similarity between samples, we assume that ...
URI: https://repository.cihe.edu.hk/jspui/handle/cihe/4105
DOI: 10.1109/TCYB.2020.3041493
CIHE Affiliated Publication: No
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