Manifold regularized extreme learning machine. Issue 2 (February 2016)

• Record Type: Journal Article
• Authors: Liu, Bing; Xia, Shi-Xiong; Meng, Fan-Rong; Zhou, Yong
• Is Part Of: Neural computing & applications. Volume 27:Issue 2(2016)
• Page Start: 255
• Page End: 269
• Publication Date: 2016-02
• Subjects: Manifold regularization -- Extreme learning machine (ELM) -- Random feature mapping -- Semi-supervised learning; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-014-1777-8
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract 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.