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 |
Appears in Collections: | CIS Publication |

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