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Unsupervised extreme learning machine with representational features

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Abstract

Extreme learning machine (ELM) is not only an effective classifier but also a useful cluster. Unsupervised extreme learning machine (US-ELM) gives favorable performance compared to state-of-the-art clustering algorithms. Extreme learning machine as an auto encoder (ELM-AE) can obtain principal components which represent original samples. The proposed unsupervised extreme learning machine based on embedded features of ELM-AE (US-EF-ELM) algorithm applies ELM-AE to US-ELM. US-EF-ELM regards embedded features of ELM-AE as the outputs of US-ELM hidden layer, and uses US-ELM to obtain the embedded matrix of US-ELM. US-EF-ELM can handle the multi-cluster clustering. The learning capability and computational efficiency of US-EF-ELM are as same as US-ELM. By experiments on UCI data sets, we compared US-EF-ELM k-means algorithm with k-means algorithm, spectral clustering algorithm, and US-ELM k-means algorithm in accuracy and efficiency.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Shifei Ding, Nan Zhang, Jian Zhang & Xinzheng Xu

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Shifei Ding, Nan Zhang, Jian Zhang, Xinzheng Xu & Zhongzhi Shi

Authors
  1. Shifei Ding
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  2. Nan Zhang
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  3. Jian Zhang
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  4. Xinzheng Xu
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  5. Zhongzhi Shi
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Correspondence to Shifei Ding.

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Ding, S., Zhang, N., Zhang, J. et al. Unsupervised extreme learning machine with representational features. Int. J. Mach. Learn. & Cyber. 8, 587–595 (2017). https://doi.org/10.1007/s13042-015-0351-8

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  • DOI: https://doi.org/10.1007/s13042-015-0351-8

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