Journal
APPLIED SOFT COMPUTING
Volume 97, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2020.106756
Keywords
Random vector functional link (RVFL); Extreme learning machine (ELM); Semi-supervised learning; Joint optimization; Electroencephalography (EEG); Emotion recognition
Categories
Funding
- Natural Science Foundation of China [61971173, U1909202, 61633010, 61972121]
- Fundamental Research Funds for the Provincial Universities of Zhejiang, China [GK209907299001-008]
- China Postdoctoral Science Foundation [2017M620470]
- Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education, Anhui Polytechnic University, China [GDSC202015]
- Acoustics Science and Technology Laboratory of Harbin Engineering University, China [SSKF2018001]
- Engineering Research Center of Cognitive Healthcare of Zhejiang Province, Sir Run Run Shaw Hospital [2018KFJJ05]
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Due to the inefficiency of gradient-based iterative ways in network training, randomization-based neural networks usually offer non-iterative closed form solutions. The random vector functional link (RVFL) and extreme learning machine (ELM) are two popular randomized networks which provide us unified frameworks for both regression and multi-class classification. Currently, existing studies on RVFL and ELM focused mainly on supervised tasks even though we usually have only a small number of labeled samples but a large number of unlabeled samples. Therefore, it is necessary to make both models appropriately utilize both labeled and unlabeled samples; that is, we should develop their semi-supervised extensions. In this paper, we propose a joint optimization framework to semi-supervised RVFL and ELM networks. In the formulated JOSRVFL (jointly optimized semi-supervised RVFL) and JOSELM, the output weight matrix and the label indicator matrix of the unlabeled samples can be jointly optimized in an iterative manner. We provide a novel approach to optimize the JOSRVFL and JOSELM objective functions. Extensive experiments on benchmark data sets and Electroencephalography-based emotion recognition tasks showed the excellent performance of the proposed JOSRVFL and JOSELM models. Moreover, because the direct input-output connections help to regularize the randomization, JOSRVFL could obtain superior performance to JOSELM in most cases. (C) 2020 Elsevier B.V. All rights reserved.
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