A Deep Neural Network Based on ELM for Semi-supervised Learning of Image Classification

Abstract

Deep learning has become one of very important machine learning methods in image classification, but most of them require a long training time to solve a non-convex optimization problem. In comparison, the training of extreme learning machine (ELM) is very simple, fast and effective. In order to combine the advantages of both methods, many researchers have tried to introduce ELM to deep architectures (Kasun et al. in IEEE Intell Syst 28:31–34, 2013; Yu et al. in Neurocomputing 149:308–315, 2015; Tissera and McDonnell in Neurocomputing 174:42–49, 2016 and in: Proceedings of ELM-2014, vol 1, Proceedings in adaptation, learning and optimization, vol 3, 2016; Junying et al. in Neurocomputing 171:63–72, 2016; Uzair et al. in Neural Comput Appl, 2015) to solve unsupervised learning and supervised learning problems. In this paper, we propose a new deep neural network based on ELM called discriminative deep ELM (DDELM) to address the semi-supervised learning problems in image classification. The proposed deep architecture consists of several stacked unsupervised ELMs and an additional label layer on the top layer of the stacked model. Experiments on three standard image data show that DDELM outperforms both representative semi-supervised learning algorithms and existing deep architectures such as DCNN in terms of accuracy and training time.