This paper presents a novel version of the hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in order to reduce the "noise" and the redundant features in the feature matrices of the image datasets and to reduce the runtime constructing the hypergraph of the hypergraph neural network method. Then, the classic graph-based semi-supervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network, the hypergraph neural network, and our proposed hypergraph neural network are employed to solve the noisy label learning problem. The accuracies of these five methods are evaluated and compared. Experimental results show that the hypergraph neural network methods achieve the best performance when the noise level increases. Moreover, the hypergraph neural network methods are at least as good as the graph neural network.
翻译:本文展示了超光速神经网络方法的新版本。 这种方法用于解决响亮标签学习问题。 首先, 我们对图像数据集的特征矩阵应用五氯苯甲醚的维度减少技术, 以减少图像数据集特征矩阵中的“ 噪音” 和冗余特性, 并减少高光线网络方法高光谱构造的运行时间。 然后, 经典的基于图形的半监督学习方法、 经典的基于超光谱的半监督学习方法、 图形神经网络、 高光学神经网络和我们提议的超光学神经网络被用于解决噪音标签学习问题。 对这五种方法的精度进行了评估和比较。 实验结果显示, 超光学神经网络方法在噪音水平上升时达到最佳性能。 此外, 高光学网络方法至少和图形神经网络一样好。