Despite the computers have developed rapidly in recent years, there are still many difficulties to obtain a large number of labelled data in many practical problems, for example, medical image diagnosis, internet fraud, and pedestrian detection. To deal with learning problems with only a few labeled data, a novel semi-supervised learning method combined with dynamic graph learning with self-paced learning mechanism is present in this work, namely SS-GSELM. Firstly, according to the loss value of labeled samples in each training, the algorithm selects the sample with the smaller loss value for learning, and then gradually adds the sample with the larger loss value during the training process until all labeled samples are trained. In particular, different weights are given to samples through a regularization function to adjust the importance of different samples on the model results. Secondly, the algorithm uses local consistency property as supplementary information to enhance the performance of the learning machine, so an adaptive graph matrix is constructed to retain data similarity information. To do this, an alternative strategy is proposed to update graph matrices and self-paced weights to adapt to the classifier. Experimental results on real data sets exhibit that the proposed method superior to the classic methods in classification tasks.

尽管计算机近年来发展迅速，但是在许多实际问题中，例如医学图像诊断，互联网欺诈和行人检测，获得大量标记数据仍然存在许多困难。为了仅用少量的标记数据来解决学习问题，这项工作提出了一种新颖的半监督学习方法，结合了具有自定进度学习机制的动态图学习，即SS-GSELM。首先，根据每次训练中标记样本的损失值，算法选择损失值较小的样本进行学习，然后在训练过程中逐渐增加损失值较大的样本，直到所有标记样本都得到训练为止。尤其是，通过正则化函数为样本赋予不同的权重，以调整不同样本对模型结果的重要性。其次，该算法利用局部一致性属性作为补充信息来提高学习机的性能，因此构造了一个自适应图矩阵来保留数据相似性信息。为此，提出了另一种策略来更新图形矩阵和自定步权数以适应分类器。在真实数据集上的实验结果表明，该方法在分类任务中优于经典方法。因此，构建了自适应图矩阵以保留数据相似性信息。为此，提出了另一种策略来更新图形矩阵和自定步权数以适应分类器。在真实数据集上的实验结果表明，该方法在分类任务中优于经典方法。因此，构建了自适应图矩阵以保留数据相似性信息。为此，提出了另一种策略来更新图形矩阵和自定步权数以适应分类器。在真实数据集上的实验结果表明，该方法在分类任务中优于经典方法。