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

doi:10.1109/IJCNN.2018.8489268

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

Finansiell ekonomi, Helsingfors

Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

5 Citeringar (Scopus)

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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.

Originalspråk	Engelska
Titel på värdpublikation	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
Volym	2018-July
Förlag	Institute of Electrical and Electronics Engineers Inc.
Utgivningsdatum	10.10.2018
Artikelnummer	8489268
ISBN (elektroniskt)	9781509060146
DOI	https://doi.org/10.1109/IJCNN.2018.8489268
Status	Publicerad - 10.10.2018
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brasilien Varaktighet: 08.07.2018 → 13.07.2018

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512 Företagsekonomi

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10.1109/IJCNN.2018.8489268

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Hu, R., Roshdibenam, V., Johnson, H. J., Eirola, E., Akusok, A., Miche, Y., Björk, K. M., & 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

@inproceedings{10bcc339f4ea454c88cc348fe6c482fd,

title = "ELM-SOM: A Continuous Self-Organizing Map for Visualization",

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.",

keywords = "512 Business and Management",

author = "Renjie Hu and Venous Roshdibenam and Johnson, {Hans J.} and Emil Eirola and Anton Akusok and Yoan Miche and Bj{\"o}rk, {Kaj Mikael} and Amaury Lendasse",

year = "2018",

month = oct,

day = "10",

doi = "10.1109/IJCNN.2018.8489268",

language = "English",

volume = "2018-July",

booktitle = "2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2018 International Joint Conference on Neural Networks, IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

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Hu, R, Roshdibenam, V, Johnson, HJ, Eirola, E, Akusok, A, Miche, Y, Björk, KM & 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., 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brasilien, 08.07.2018. https://doi.org/10.1109/IJCNN.2018.8489268

ELM-SOM : A Continuous Self-Organizing Map for Visualization. / Hu, Renjie; Roshdibenam, Venous; Johnson, Hans J. et al.

2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. Vol. 2018-July Institute of Electrical and Electronics Engineers Inc., 2018. 8489268.

Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

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T2 - 2018 International Joint Conference on Neural Networks, IJCNN 2018

AU - Hu, Renjie

AU - Roshdibenam, Venous

AU - Johnson, Hans J.

AU - Eirola, Emil

AU - Akusok, Anton

AU - Miche, Yoan

AU - Björk, Kaj Mikael

AU - Lendasse, Amaury

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N2 - 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.

AB - 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.

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BT - 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings

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

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