NDLI: Manifold regularized extreme learning machine

Content Provider	Springer Nature Link
Author	Liu, Bing Xia, Shi Xiong Meng, Fan Rong Zhou, Yong
Copyright Year	2015
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.
Starting Page	255
Ending Page	269
Page Count	15
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	27
Issue Number	2
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2015-02-28
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Manifold regularization Extreme learning machine (ELM) Random feature mapping Semi-supervised learning Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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