IEEE Trans Cybern. 2019 Apr;49(4):1440-1453. doi: 10.1109/TCYB.2018.2804326. Epub 2018 Feb 27.
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.
半监督学习（SSL）方法通过利用大量未标记的样本和少量标记的样本，已经被证明在解决标记样本不足的问题上非常有效。然而，许多传统的 SSL 方法可能对过多的标注噪声数据不够稳健。为了解决这个问题，在本文中，我们提出了一种基于最大相关熵准则的鲁棒图基 SSL 方法，以学习一个稳健且具有强泛化能力的模型。具体来说，通过在正则项上施加监督信息来改进基于图的 SSL 框架，这可以加强对标签的约束，从而确保每个聚类的预测标签接近真实标签。此外，将最大相关熵准则引入到基于图的 SSL 框架中，以抑制标注噪声。广泛的图像分类实验证明了所提出的 SSL 方法的泛化性和稳健性。
