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

Finance, Helsinki

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

5 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 language	English
Title of host publication	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
Volume	2018-July
Publisher	Institute of Electrical and Electronics Engineers Inc.
Publication date	10.10.2018
Article number	8489268
ISBN (Electronic)	9781509060146
DOIs	https://doi.org/10.1109/IJCNN.2018.8489268
Publication status	Published - 10.10.2018
MoE publication type	A4 Article in conference proceedings
Event	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil Duration: 08.07.2018 → 13.07.2018

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512 Business and Management

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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. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings (Vol. 2018-July). Article 8489268 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8489268

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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",

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doi = "10.1109/IJCNN.2018.8489268",

language = "English",

volume = "2018-July",

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

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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. in 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, Brazil, 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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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AU - Johnson, Hans J.

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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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PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 8 July 2018 through 13 July 2018

ER -

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

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