Manifold regularized extreme learning machine

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• 作者：Bing Liu ; Shi-Xiong Xia ; Fan-Rong Meng ; Yong Zhou
• 关键词：Manifold regularization ; Extreme learning machine (ELM) ; Random feature mapping ; Semi ; supervised learning
• 刊名：Neural Computing & Applications
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：27
• 期：2
• 页码：255-269
• 全文大小：2,348 KB
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• 作者单位：Bing Liu (1)
  Shi-Xiong Xia (1)
  Fan-Rong Meng (1)
  Yong Zhou (1)
  
  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
  
• 刊物类别：Computer Science
• 刊物主题：Simulation and Modeling
  
• 出版者：Springer London
• ISSN：1433-3058

文摘

Extreme learning machine (ELM) works for generalized single-hidden-layer feedforward networks (SLFNs), and its essence is that the hidden layer of SLFNs need not be tuned. But ELM only utilizes labeled data to carry out the supervised learning task. In order to exploit unlabeled data in the ELM model, we first extend the manifold regularization (MR) framework and then demonstrate the relation between the extended MR framework and ELM. Finally, a manifold regularized extreme learning machine is derived from the proposed framework, which maintains the properties of ELM and can be applicable to large-scale learning problems. Experimental results show that the proposed semi-supervised extreme learning machine is the most cost-efficient method. It tends to have better scalability and achieve satisfactory generalization performance at a relatively faster learning speed than traditional semi-supervised learning algorithms. Keywords Manifold regularization Extreme learning machine (ELM) Random feature mapping Semi-supervised learning