Applied Sciences (Apr 2021)

Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine

  • Zhewei Liu,
  • Zijia Zhang,
  • Yaoming Cai,
  • Yilin Miao,
  • Zhikun Chen

DOI
https://doi.org/10.3390/app11093867
Journal volume & issue
Vol. 11, no. 9
p. 3867

Abstract

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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.

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