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

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Sammanfattning

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.

OriginalspråkEngelska
Titel på gästpublikation2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
Volym2018-July
FörlagInstitute of Electrical and Electronics Engineers Inc.
Utgivningsdatum10.10.2018
Artikelnummer8489268
ISBN (elektroniskt)9781509060146
DOI
StatusPublicerad - 10.10.2018
MoE-publikationstypA4 Artikel i en konferenspublikation
Evenemang2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brasilien
Varaktighet: 08.07.201813.07.2018

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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. I 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