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Extreme Learning Machines for VISualization+R: Mastering Visualization with Target Variables

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Abstract

The current paper presents an improvement of the Extreme Learning Machines for VISualization (ELMVIS+) nonlinear dimensionality reduction method. In this improved method, called ELMVIS+R, it is proposed to apply the originally unsupervised ELMVIS+ method for the regression problems, using target values to improve visualization results. It has been shown in previous work that the approach of adding supervised component for classification problems indeed allows to obtain better visualization results. To verify this assumption for regression problems, a set of experiments on several different datasets was performed. The newly proposed method was compared to the ELMVIS+ method and, in most cases, outperformed the original algorithm. Results, presented in this article, prove the general idea that using supervised components (target values) with nonlinear dimensionality reduction method like ELMVIS+ can improve both visual properties and overall accuracy.

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA, USA

    Andrey Gritsenko, Stephen Baek & Amaury Lendasse

  2. The Iowa Informatics Initiative, The University of Iowa, Iowa City, IA, USA

    Andrey Gritsenko & Amaury Lendasse

  3. Risklab at Arcada University of Applied Sciences, Helsinki, Finland

    Anton Akusok & Amaury Lendasse

  4. Nokia Bell Laboratorys, Helsinki, Finland

    Yoan Miche

Authors
  1. Andrey Gritsenko
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  2. Anton Akusok
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  3. Stephen Baek
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  4. Yoan Miche
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  5. Amaury Lendasse
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Correspondence to Andrey Gritsenko.

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Gritsenko, A., Akusok, A., Baek, S. et al. Extreme Learning Machines for VISualization+R: Mastering Visualization with Target Variables. Cogn Comput 10, 464–477 (2018). https://doi.org/10.1007/s12559-017-9537-6

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  • DOI: https://doi.org/10.1007/s12559-017-9537-6

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