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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-06

AUTHORS

Nan Zhang, Shifei Ding

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. More... »

PAGES

129-139

References to SciGraph publications

  • 2014-09. Multitask Extreme Learning Machine for Visual Tracking in COGNITIVE COMPUTATION
  • 2014-09. Extreme learning machine and its applications in NEURAL COMPUTING AND APPLICATIONS
  • 2007-12. A tutorial on spectral clustering in STATISTICS AND COMPUTING
  • 1998. Characterising Virtual Eigensignatures for General Purpose Face Recognition in FACE RECOGNITION
  • 2014-11. Wavelet twin support vector machine in NEURAL COMPUTING AND APPLICATIONS
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    http://scigraph.springernature.com/pub.10.1007/s12293-016-0198-x

    DOI

    http://dx.doi.org/10.1007/s12293-016-0198-x

    DIMENSIONS

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