Unsupervised neural network based topological learning from point clouds for map building

Yuichiro Toda; Weihong Chin; Naoyuki Kubota

doi:10.1109/MHS.2017.8305188

Unsupervised neural network based topological learning from point clouds for map building

Yuichiro Toda, Weihong Chin, Naoyuki Kubota

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Topological structure learning methods are expected for the field of data mining for extracting multiscale topological structures from an unknown dataset. In this paper, we introduce the unsupervised neural network method for topological structure learning method from point clouds for map building. We propose Batch Learning GNG (BL-GNG) in order to improve the learning convergence. BL-GNG uses an objective function based on Fuzzy C-means for improving the learning convergence. Finally, we conduct on several experiments for evaluating our proposed method by comparing to other hierarchical approaches, and discuss the effectiveness of our proposed method.

Original language	English
Title of host publication	MHS 2017 - 28th 2017 International Symposium on Micro-NanoMechatronics and Human Science
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781538633144
DOIs	https://doi.org/10.1109/MHS.2017.8305188
Publication status	Published - Feb 28 2018
Externally published	Yes
Event	28th International Symposium on Micro-NanoMechatronics and Human Science, MHS 2017 - Nagoya, Japan Duration: Dec 3 2017 → Dec 6 2017

Publication series

Name	MHS 2017 - 28th 2017 International Symposium on Micro-NanoMechatronics and Human Science
Volume	2018-January

Other

Other	28th International Symposium on Micro-NanoMechatronics and Human Science, MHS 2017
Country/Territory	Japan
City	Nagoya
Period	12/3/17 → 12/6/17

ASJC Scopus subject areas

Biotechnology
Biomedical Engineering
Electrical and Electronic Engineering
Mechanical Engineering

Access to Document

10.1109/MHS.2017.8305188

Cite this

Toda, Y., Chin, W., & Kubota, N. (2018). Unsupervised neural network based topological learning from point clouds for map building. In MHS 2017 - 28th 2017 International Symposium on Micro-NanoMechatronics and Human Science (pp. 1-6). (MHS 2017 - 28th 2017 International Symposium on Micro-NanoMechatronics and Human Science; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MHS.2017.8305188

Unsupervised neural network based topological learning from point clouds for map building. / Toda, Yuichiro; Chin, Weihong; Kubota, Naoyuki.

MHS 2017 - 28th 2017 International Symposium on Micro-NanoMechatronics and Human Science. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6 (MHS 2017 - 28th 2017 International Symposium on Micro-NanoMechatronics and Human Science; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Toda, Y, Chin, W & Kubota, N 2018, Unsupervised neural network based topological learning from point clouds for map building. in MHS 2017 - 28th 2017 International Symposium on Micro-NanoMechatronics and Human Science. MHS 2017 - 28th 2017 International Symposium on Micro-NanoMechatronics and Human Science, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 28th International Symposium on Micro-NanoMechatronics and Human Science, MHS 2017, Nagoya, Japan, 12/3/17. https://doi.org/10.1109/MHS.2017.8305188

Toda Y, Chin W, Kubota N. Unsupervised neural network based topological learning from point clouds for map building. In MHS 2017 - 28th 2017 International Symposium on Micro-NanoMechatronics and Human Science. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-6. (MHS 2017 - 28th 2017 International Symposium on Micro-NanoMechatronics and Human Science). https://doi.org/10.1109/MHS.2017.8305188

Toda, Yuichiro ; Chin, Weihong ; Kubota, Naoyuki. / Unsupervised neural network based topological learning from point clouds for map building. MHS 2017 - 28th 2017 International Symposium on Micro-NanoMechatronics and Human Science. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-6 (MHS 2017 - 28th 2017 International Symposium on Micro-NanoMechatronics and Human Science).

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abstract = "Topological structure learning methods are expected for the field of data mining for extracting multiscale topological structures from an unknown dataset. In this paper, we introduce the unsupervised neural network method for topological structure learning method from point clouds for map building. We propose Batch Learning GNG (BL-GNG) in order to improve the learning convergence. BL-GNG uses an objective function based on Fuzzy C-means for improving the learning convergence. Finally, we conduct on several experiments for evaluating our proposed method by comparing to other hierarchical approaches, and discuss the effectiveness of our proposed method.",

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AB - Topological structure learning methods are expected for the field of data mining for extracting multiscale topological structures from an unknown dataset. In this paper, we introduce the unsupervised neural network method for topological structure learning method from point clouds for map building. We propose Batch Learning GNG (BL-GNG) in order to improve the learning convergence. BL-GNG uses an objective function based on Fuzzy C-means for improving the learning convergence. Finally, we conduct on several experiments for evaluating our proposed method by comparing to other hierarchical approaches, and discuss the effectiveness of our proposed method.

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Unsupervised neural network based topological learning from point clouds for map building

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