  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.
