Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 49, Issue 4, Pages 1440-1453Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2804326
Keywords
Graph semisupervised learning; half quadratic (HQ) optimization; maximum correntropy criterion; robust
Categories
Funding
- National Natural Science Foundation of China [61471274, 41431175, 61771349, 61711530239]
- Australian Research Council [FL-170100117, DP-180103424, DP-140102164, LP-150100671]
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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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