Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine

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

doi:10.3390/app11093867

Abstract

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.

Highlights

IntroductionExtreme Learning Machine (ELM) [1,2] was developed as a simple but effective learning model for classification and regression problems
Extreme Learning Machine (ELM) [1,2] was developed as a simple but effective learning model for classification and regression problems. As it is a special form of random vector functional-link network (RVFL) [3], ELM suggests that the hidden layer parameters of a neural network play an important role but does not need update during training [4,5]
We evaluate our method on 26 widely used classification datasets taken from University of California at Irvine (UCI) repository

Summary

Introduction

Extreme Learning Machine (ELM) [1,2] was developed as a simple but effective learning model for classification and regression problems. As it is a special form of random vector functional-link network (RVFL) [3], ELM suggests that the hidden layer parameters of a neural network play an important role but does not need update during training [4,5]. A large number of ELM variants have been proposed and widely applied to biomedical data analysis [6], computer vision [7], system modeling and prediction [8,9], and so on

Methods

Results

Conclusion