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

Andrey Gritsenko*, Anton Akusok, Stephen Baek, Yoan Miche, Amaury Lendasse

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review


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.

Original languageEnglish
Peer-reviewed scientific journalCognitive Computation
Issue number3
Pages (from-to)464-477
Number of pages14
Publication statusPublished - 22.12.2017
MoE publication typeA1 Journal article - refereed


  • 512 Business and Management
  • Nonlinear regression
  • Machine Learning
  • Artificial neural networks
  • Extreme learning machines
  • Nonlinear dimensionality reduction
  • Cosine similarity


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