Combined nonlinear visualization and classification: ELMVIS++C

Andrey Gritsenko, Anton Akusok, Yoan Miche, Kaj-Mikael Björk, Stephen Baek, Amaury Lendasse

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)

Abstract

This paper presents an improvement of the ELMVIS+ method that is proposed for fast nonlinear dimensionality reduction. The ELMVIS++C has an additional supervised learning component compared to ELMVIS+, which is originally an unsupervised method as like the majority of the other dimensionality reduction method. This component prevents samples under the same class being separated apart from each other. In this improved method, the importance of the supervised component can be further tuned to have different level of influence. The test results on four datasets indicate that the proposed improvement not only maintains the performance of ELMVIS+, but also is extremely beneficial for certain applications where the visualization of the data in relation with the class becomes an important issue.
Original languageEnglish
Title of host publication 2016 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
Publication date2016
ISBN (Print) 978-1-5090-0621-2
ISBN (Electronic)978-1-5090-0620-5 , 978-1-5090-0619-9
DOIs
Publication statusPublished - 2016
MoE publication typeA4 Article in conference proceedings
Event 2016 International Joint Conference on Neural Networks (IJCNN) - Vancouver, Canada
Duration: 24.07.201629.07.2016

Keywords

  • 512 Business and Management
  • Data visualization
  • Distributed databases
  • Mathematical model
  • Urban areas
  • Principal component analysis
  • Cost function

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