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International Conference on Neural Information Processing

ICONIP 2020: Neural Information Processing pp 11–20Cite as

A Factorized Extreme Learning Machine and Its Applications in EEG-Based Emotion Recognition

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1333))

Abstract

Extreme learning machine (ELM) is an efficient learning algorithm for single hidden layer feed forward neural networks. Its main feature is the random generation of the hidden layer weights and biases and then we only need to determine the output weights in model learning. However, the random mapping in ELM impairs the discriminative information of data to certain extent, which brings side effects for the output weight matrix to well capture the essential data properties. In this paper, we propose a factorized extreme learning machine (FELM) by incorporating another hidden layer between the ELM hidden layer and the output layer. Mathematically, the original output matrix is factorized so as to effectively explore the structured discriminative information of data. That is, we constrain the group sparsity of data representation in the new hidden layer, which will be further projected to the output layer. An efficient learning algorithm is proposed to optimize the objective of the proposed FELM model. Extensive experiments on EEG-based emotion recognition show the effectiveness of FELM.

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Acknowledgements

This work was supported by NSFC (61971173,U1909202), Fundamental Research Funds for the Provincial Universities of Zhejiang (GK209907299001-008), Postdoctoral Science Foundation of China (2017M620470), Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education, Anhui Polytechnic University (GDSC202015) and Provincial Key Laboratory for Computer Information Processing Technology, Soochow University (KJS1841).

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Authors and Affiliations

  1. Center for OPTIMAL, Northwestern Polytechnical University, Xi’an, 710072, China

    Yong Peng & Feiping Nie

  2. School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, China

    Yong Peng, Rixin Tang & Wanzeng Kong

  3. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, China

    Yong Peng

Authors
  1. Yong Peng
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  2. Rixin Tang
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  3. Wanzeng Kong
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  4. Feiping Nie
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Corresponding author

Correspondence to Yong Peng .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

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Cite this paper

Peng, Y., Tang, R., Kong, W., Nie, F. (2020). A Factorized Extreme Learning Machine and Its Applications in EEG-Based Emotion Recognition. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_2

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  • DOI: https://doi.org/10.1007/978-3-030-63823-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63822-1

  • Online ISBN: 978-3-030-63823-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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