ELMVIS+: Improved Nonlinear Visualization Technique Using Cosine Distance and Extreme Learning Machines

Anton Akusok, Yoan Miche, Kaj-Mikael Björk, Rui Nian, Paula Lauren, Amaury Lendasse

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

Abstract

This paper presents ELMVIS+, a significant improvement in ELMVIS methodology that enables faster computation, more stable results and a wider application range. The novel cost function and a fast way of estimating it speeds up the method compared to ELMVIS, especially in large-dimensional datasets. The included Genetic Algorithms add global optimization that helps ELMVIS+ to find a better optimum. The improved methodology shows state-of-the-art performance in three different benchmark datasets.
Original languageEnglish
Title of host publicationProceedings of ELM-2015
Number of pages13
Volume2
Place of PublicationCham
PublisherSpringer
Publication date03.01.2016
Pages357-369
ISBN (Print)978-3-319-28372-2
ISBN (Electronic)978-3-319-28373-9
DOIs
Publication statusPublished - 03.01.2016
MoE publication typeA4 Article in conference proceedings

Publication series

Name Proceedings in Adaptation, Learning and Optimization (PALO)
Volume7

Keywords

  • 512 Business and Management
  • Visualization
  • Nonlinear dimensionality reduction
  • Machine learning
  • Neural network
  • Genetic algorithms
  • Cosine distance
  • Extreme learning machines
  • Big data
  • Big dimensionality
  • Projection

Fingerprint Dive into the research topics of 'ELMVIS+: Improved Nonlinear Visualization Technique Using Cosine Distance and Extreme Learning Machines'. Together they form a unique fingerprint.

  • Cite this

    Akusok, A., Miche, Y., Björk, K-M., Nian, R., Lauren, P., & Lendasse, A. (2016). ELMVIS+: Improved Nonlinear Visualization Technique Using Cosine Distance and Extreme Learning Machines. In Proceedings of ELM-2015 (Vol. 2, pp. 357-369 ). ( Proceedings in Adaptation, Learning and Optimization (PALO); Vol. 7). Springer. https://doi.org/10.1007/978-3-319-28373-9_31