Robust Graph-Based Semisupervised Learning for Noisy Labeled Data via Maximum Correntropy Criterion
针对传统半监督学习在标记数据含噪声时鲁棒性不足的问题，提出一种基于最大相关熵准则的图半监督方法，通过改进正则项和引入噪声抑制机制，提升模型泛化能力和鲁棒性，图像分类实验验证了其有效性。
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.