Noise-robust classification with hypergraph neural network

TL;DR

This paper introduces a noise-robust hypergraph neural network that employs PCA for feature reduction, demonstrating superior performance in noisy label learning scenarios compared to existing methods.

Contribution

The paper proposes a novel hypergraph neural network approach combined with PCA for improved noise robustness in label learning tasks.

Findings

Hypergraph neural networks outperform other methods as noise increases.

PCA reduces feature noise and computational complexity.

Hypergraph neural networks are at least as effective as graph neural networks.

Abstract

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.