Robust Graph-Based Semisupervised Learning for Noisy Labeled Data via Maximum Correntropy Criterion.

Du, B; Xinyao, T; Wang, Z; Zhang, L; Tao, D

Robust Graph-Based Semisupervised Learning for Noisy Labeled Data via Maximum Correntropy Criterion.

Du, B Xinyao, T Wang, Z Zhang, L Tao, D

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Cybernetics, 2019, 49, (4), pp. 1440-1453
Issue Date:: 2019-04

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Field	Value	Language
dc.contributor.author	Du, B
dc.contributor.author	Xinyao, T
dc.contributor.author	Wang, Z
dc.contributor.author	Zhang, L
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449
dc.date.accessioned	2020-05-12T06:11:43Z
dc.date.available	2020-05-12T06:11:43Z
dc.date.issued	2019-04
dc.identifier.citation	IEEE Transactions on Cybernetics, 2019, 49, (4), pp. 1440-1453
dc.identifier.issn	2168-2267
dc.identifier.issn	2168-2275
dc.identifier.uri	http://hdl.handle.net/10453/140671
dc.description.abstract	Semisupervised learning (SSL) methods have been proved to be effective at solving the labeled samples shortage problem by using a large number of unlabeled samples together with a small number of labeled samples. However, many traditional SSL methods may not be robust with too much labeling noisy data. To address this issue, in this paper, we propose a robust graph-based SSL method based on maximum correntropy criterion to learn a robust and strong generalization model. In detail, the graph-based SSL framework is improved by imposing supervised information on the regularizer, which can strengthen the constraint on labels, thus ensuring that the predicted labels of each cluster are close to the true labels. Furthermore, the maximum correntropy criterion is introduced into the graph-based SSL framework to suppress labeling noise. Extensive image classification experiments prove the generalization and robustness of the proposed SSL method.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Cybernetics
dc.relation.isbasedon	10.1109/tcyb.2018.2804326
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Robust Graph-Based Semisupervised Learning for Noisy Labeled Data via Maximum Correntropy Criterion.
dc.type	Journal Article
utslib.citation.volume	49
utslib.location.activity	United States
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-12T06:11:42Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	49
utslib.start-page	1440
utslib.citation.issue	4

Abstract:

Semisupervised learning (SSL) methods have been proved to be effective at solving the labeled samples shortage problem by using a large number of unlabeled samples together with a small number of labeled samples. However, many traditional SSL methods may not be robust with too much labeling noisy data. To address this issue, in this paper, we propose a robust graph-based SSL method based on maximum correntropy criterion to learn a robust and strong generalization model. In detail, the graph-based SSL framework is improved by imposing supervised information on the regularizer, which can strengthen the constraint on labels, thus ensuring that the predicted labels of each cluster are close to the true labels. Furthermore, the maximum correntropy criterion is introduced into the graph-based SSL framework to suppress labeling noise. Extensive image classification experiments prove the generalization and robustness of the proposed SSL method.

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http://hdl.handle.net/10453/140671