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Cognitive Computation

Erschienen in:

22.12.2017

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

verfasst von: Andrey Gritsenko, Anton Akusok, Stephen Baek, Yoan Miche, Amaury Lendasse

Erschienen in: Cognitive Computation | Ausgabe 3/2018

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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.

Vorheriger Artikel Multiple Attribute Decision-Making Methods Based on the Expected Value and the Similarity Measure of Hesitant Neutrosophic Linguistic Numbers

Nächster Artikel Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm

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Titel: Extreme Learning Machines for VISualization+R: Mastering Visualization with Target Variables
verfasst von: Andrey Gritsenko
Anton Akusok
Stephen Baek
Yoan Miche
Amaury Lendasse
Publikationsdatum: 22.12.2017
Verlag: Springer US
Erschienen in: Cognitive Computation / Ausgabe 3/2018
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-017-9537-6

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