Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine

• Main Authors: Zhewei Liu, Zijia Zhang, Yaoming Cai, Yilin Miao, Zhikun Chen
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Applied Sciences
• Subjects: graph convolutional network; extreme learning machine; semi-supervised learning; hypergraph learning
• Online Access: https://www.mdpi.com/2076-3417/11/9/3867

Extreme Learning Machine (ELM) is characterized by simplicity, generalization ability, and computational efficiency. However, previous ELMs fail to consider the inherent high-order relationship among data points, resulting in being powerless on structured data and poor robustness on noise data. This paper presents a novel semi-supervised ELM, termed Hypergraph Convolutional ELM (HGCELM), based on using hypergraph convolution to extend ELM into the non-Euclidean domain. The method inherits all the advantages from ELM, and consists of a random hypergraph convolutional layer followed by a hypergraph convolutional regression layer, enabling it to model complex intraclass variations. We show that the traditional ELM is a special case of the HGCELM model in the regular Euclidean domain. Extensive experimental results show that HGCELM remarkably outperforms eight competitive methods on 26 classification benchmarks.