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Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data

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Abstract

Extreme learning machine (ELM) not only is an effective classifier in supervised learning, but also can be applied on unsupervised learning and semi-supervised learning. The model structure of unsupervised extreme learning machine (US-ELM) and semi-supervised extreme learning machine (SS-ELM) are same as ELM, the difference between them is the cost function. We introduce kernel function to US-ELM and propose unsupervised extreme learning machine with kernel (US-KELM). And SS-KELM has been proposed. Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and Wavelet kernel functions have been widely used in support vector machine. Therefore, to realize a combination of the wavelet kernel function, US-ELM, and SS-ELM, unsupervised extreme learning machine with wavelet kernel function (US-WKELM) and semi-supervised extreme learning machine with wavelet kernel function (SS-WKELM) are proposed in this paper. The experimental results show the feasibility and validity of US-WKELM and SS-WKELM in clustering and classification.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No.61379101).

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Authors and Affiliations

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

    Nan Zhang & Shifei Ding

  2. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining and Technology, Xuzhou, 221116, China

    Nan Zhang & Shifei Ding

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  1. Nan Zhang
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  2. Shifei Ding
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Correspondence to Shifei Ding.

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Zhang, N., Ding, S. Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data. Memetic Comp. 9, 129–139 (2017). https://doi.org/10.1007/s12293-016-0198-x

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  • DOI: https://doi.org/10.1007/s12293-016-0198-x

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