ELM-SOM: A Continuous Self-Organizing Map for Visualization

Renjie Hu, Venous Roshdibenam, Hans J. Johnson, Emil Eirola, Anton Akusok, Yoan Miche, Kaj Mikael Björk, Amaury Lendasse

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

3 Citations (Scopus)

Abstract

This paper presents a novel dimensionality reduction technique: ELM-SOM. This technique preserves the intrinsic quality of Self-Organizing Maps (SOM): It is nonlinear and suitable for big data. It also brings continuity to the projection using two Extreme Learning Machine (ELM) models, the first one to perform the dimensionality reduction and the second one to perform the reconstruction. ELM-SOM is tested successfully on six diverse datasets. Regarding reconstruction error, ELM-SOM is comparable to SOM while bringing continuity.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
Volume2018-July
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date10.10.2018
Article number8489268
ISBN (Electronic)9781509060146
DOIs
Publication statusPublished - 10.10.2018
MoE publication typeA4 Article in conference proceedings
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 08.07.201813.07.2018

    Fingerprint

Keywords

  • 512 Business and Management

Cite this

Hu, R., Roshdibenam, V., Johnson, H. J., Eirola, E., Akusok, A., Miche, Y., ... Lendasse, A. (2018). ELM-SOM: A Continuous Self-Organizing Map for Visualization. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings (Vol. 2018-July). [8489268] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8489268