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Domain Adaptation Transfer Extreme Learning Machines

  • Conference paper
Proceedings of ELM-2014 Volume 1

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 3))

Abstract

Extreme learning machines (ELMs) have been confirmed to be efficient and effective learning techniques for pattern recognition and regression. However, ELMs primarily focus on the supervised, semi-supervised and unsupervised learning problems in single domain and the generalization ability in multiple domains based learning issues is hardly studied. This paper aims to propose a unified framework of ELMs with domain adaptation and improve their transfer learning capability in cross domains without loss of the computational efficiency of traditional ELMs. We integrate domain adaptation into ELMs and two algorithms including source domain adaptation transfer ELM (TELM-SDA) and target domain adaptation transfer ELM (TELM-TDA) are proposed. For insight of the difference among ELM, TELM-SDA and TELM-TDA, two remarks are provided. Experiments on the popular sensor drift big data with multiple batches in machine olfaction, the results clearly demonstrate the characteristics of the proposed domain adaptation transfer ELMs that they can not only copy with sensor drift efficiently without cumbersome measures comparable to state-of-the-art methods but also bring new perspectives for ELM.

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Author information

Authors and Affiliations

  1. College of Computer Science, Chongqing University, No.174 Shazheng Street, ShapingBa District, Chongqing, China

    Lei Zhang

  2. Department of Computing, The Hong Kong Polytechnic Unviersity, Hung Hom, Hong Kong

    Lei Zhang & David Zhang

Authors
  1. Lei Zhang
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  2. David Zhang
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Corresponding author

Correspondence to Lei Zhang .

Editor information

Editors and Affiliations

  1. Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

  3. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

  4. Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia

    Zhihong Man

  5. School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of (South Korea)

    Kar-Ann Toh

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© 2015 Springer International Publishing Switzerland

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Zhang, L., Zhang, D. (2015). Domain Adaptation Transfer Extreme Learning Machines. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-14063-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-14063-6_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14062-9

  • Online ISBN: 978-3-319-14063-6

  • eBook Packages: EngineeringEngineering (R0)

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