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Multi-label Text Categorization Using \(L_{21}\)-norm Minimization Extreme Learning Machine

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Proceedings of ELM-2015 Volume 1

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 6))

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Abstract

Extreme learning machine (ELM) was extended from the generalized single hidden layer feedforward networks where the input weights of the hidden layer nodes can be assigned randomly. It has been widely used for its much faster learning speed and less manual works. Considering the field of multi-label text classification, in this paper, we propose an ELM based algorithm combined with \(L_{21}\)-norm minimization of the output weights matrix called \(L_{21}\)-norm Minimization ELM, which not only fully inherits the merits of ELM but also facilitates group sparsity and reduces complexity of the learning model. Extensive experiments on several benchmark data sets show a more desirable performance compared with other common multi-label classification algorithms.

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Authors and Affiliations

  1. College of Command Information System, PLA University of Science and Technology, Nanjing, 210007, China

    Mingchu Jiang, Na Li & Zhisong Pan

Authors
  1. Mingchu Jiang
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  2. Na Li
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  3. Zhisong Pan
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Corresponding author

Correspondence to Mingchu Jiang .

Editor information

Editors and Affiliations

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

    Jiuwen Cao

  2. Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

  3. ECE, U of Windsor, WINDSOR, Ontario, Canada

    Jonathan Wu

  4. Dept of Mechanical and Industrial Engg, University of Iowa, Iowa City, Iowa, USA

    Amaury Lendasse

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Jiang, M., Li, N., Pan, Z. (2016). Multi-label Text Categorization Using \(L_{21}\)-norm Minimization Extreme Learning Machine. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-28397-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-28397-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28396-8

  • Online ISBN: 978-3-319-28397-5

  • eBook Packages: EngineeringEngineering (R0)

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