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 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781538633144 |
| DOIs | |
| 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 |
Other
| Other | 28th International Symposium on Micro-NanoMechatronics and Human Science, MHS 2017 |
|---|---|
| Country | Japan |
| City | Nagoya |
| Period | 12/3/17 → 12/6/17 |
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ASJC Scopus subject areas
- Biotechnology
- Biomedical Engineering
- Electrical and Electronic Engineering
- Mechanical Engineering
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 (Vol. 2018-January, pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MHS.2017.8305188