Title: Semi-supervised extreme learning machine with wavelet kernel

Authors: Nan Zhang

Addresses: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

Abstract: Extreme learning machine (ELM) not only is an effective classifier in supervised learning, but also can be applied on unsupervised learning and semi-supervised learning. The model structure of semi-supervised machine learning machine (SS-ELM) is same as ELM, the difference between them is the cost function. In this paper, we introduce kernel function to SS-ELM and proposed semi-supervised extreme learning machine with kernel (SS-KELM). Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and the wavelet kernel is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Therefore, we propose semi-supervised extreme learning machine with wavelet kernel (SS-WKELM) based on the wavelet kernel function and SS-ELM. The experimental results show the feasibility and validity of SS-WKELM in classification.

Keywords: extreme learning machine; kernel function; wavelet function; semi-supervised learning.

DOI: 10.1504/IJCI.2016.084105

International Journal of Collaborative Intelligence, 2016 Vol.1 No.4, pp.298 - 308

Received: 15 Jan 2016
Accepted: 16 Mar 2016
Published online: 12 May 2017 *

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