ELM-SOM+: A continuous mapping for visualization

Renjie Hu; Karl Ratner; Edward Ratner; Yoan Miche; Kaj Mikael Björk; Amaury Lendasse

doi:10.1016/j.neucom.2019.06.093

ELM-SOM+: A continuous mapping for visualization

Renjie Hu, Karl Ratner, Edward Ratner, Yoan Miche, Kaj Mikael Björk, Amaury Lendasse

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

This paper presents a novel dimensionality reduction technique based on ELM and SOM: ELM-SOM+. This technique preserves the intrinsic quality of Self-Organizing Map (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 nine diverse datasets. Regarding reconstruction error, the new methodology shows considerable improvement over SOM and brings continuity.

Original language	English (US)
Pages (from-to)	147-156
Number of pages	10
Journal	Neurocomputing
Volume	365
DOIs	https://doi.org/10.1016/j.neucom.2019.06.093
State	Published - Nov 6 2019

Keywords

Dimensionality reduction techniques
Extreme Learning Machines
Machine learning
Neural networks
Self-Organizing Maps
Visualization

ASJC Scopus subject areas

Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence

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10.1016/j.neucom.2019.06.093

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title = "ELM-SOM+: A continuous mapping for visualization",

abstract = "This paper presents a novel dimensionality reduction technique based on ELM and SOM: ELM-SOM+. This technique preserves the intrinsic quality of Self-Organizing Map (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 nine diverse datasets. Regarding reconstruction error, the new methodology shows considerable improvement over SOM and brings continuity.",

keywords = "Dimensionality reduction techniques, Extreme Learning Machines, Machine learning, Neural networks, Self-Organizing Maps, Visualization",

author = "Renjie Hu and Karl Ratner and Edward Ratner and Yoan Miche and Bj{\"o}rk, \{Kaj Mikael\} and Amaury Lendasse",

note = "Publisher Copyright: {\textcopyright} 2019 Elsevier B.V.",

year = "2019",

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doi = "10.1016/j.neucom.2019.06.093",

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T1 - ELM-SOM+

T2 - A continuous mapping for visualization

AU - Hu, Renjie

AU - Ratner, Karl

AU - Ratner, Edward

AU - Miche, Yoan

AU - Björk, Kaj Mikael

AU - Lendasse, Amaury

PY - 2019/11/6

Y1 - 2019/11/6

N2 - This paper presents a novel dimensionality reduction technique based on ELM and SOM: ELM-SOM+. This technique preserves the intrinsic quality of Self-Organizing Map (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 nine diverse datasets. Regarding reconstruction error, the new methodology shows considerable improvement over SOM and brings continuity.

AB - This paper presents a novel dimensionality reduction technique based on ELM and SOM: ELM-SOM+. This technique preserves the intrinsic quality of Self-Organizing Map (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 nine diverse datasets. Regarding reconstruction error, the new methodology shows considerable improvement over SOM and brings continuity.

KW - Dimensionality reduction techniques

KW - Extreme Learning Machines

KW - Machine learning

KW - Neural networks

KW - Self-Organizing Maps

KW - Visualization

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DO - 10.1016/j.neucom.2019.06.093

M3 - Article

AN - SCOPUS:85069956302

SN - 0925-2312

VL - 365

SP - 147

EP - 156

JO - Neurocomputing

JF - Neurocomputing

ER -

ELM-SOM+: A continuous mapping for visualization

Abstract

Keywords

ASJC Scopus subject areas

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