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https://doi.org/10.21256/zhaw-3532| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Clustered multidimensional scaling with Rulkov neurons |
| Authors: | Ott, Thomas Schüle, Martin Held, Jenny Albert, Carlo Stoop, Ruedi |
| DOI: | 10.21256/zhaw-3532 |
| Proceedings: | 2016 International Symposium on Nonlinear Theory and Its Applications |
| Page(s): | 389 |
| Pages to: | 392 |
| Conference details: | Nonlinear Theory and Applications 2016 (NOLTA), Yugawara, Japan, 27-30 November 2016 |
| Issue Date: | 2016 |
| Publisher / Ed. Institution: | IEICE |
| Language: | English |
| Subjects: | Clustering; Neural Network; Dimensionality Reduction |
| Subject (DDC): | 006: Special computer methods |
| Abstract: | When dealing with high-dimensional measurements that often show non-linear characteristics at multiple scales, a need for unbiased and robust classification and interpretation techniques has emerged. Here, we present a method for mapping high-dimensional data onto low-dimensional spaces, allowing for a fast visual interpretation of the data. Classical approaches of dimensionality reduction attempt to preserve the geometry of the data. They often fail to correctly grasp cluster structures, for instance in high-dimensional situations, where distances between data points tend to become more similar. In order to cope with this clustering problem, we propose to combine classical multi-dimensional scaling with data clustering based on self-organization processes in neural networks, where the goal is to amplify rather than preserve local cluster structures. We find that applying dimensionality reduction techniques to the output of neural network based clustering not only allows for a convenient visual inspection, but also leads to further insights into the intraand inter-cluster connectivity. We report on an implementation of the method with Rulkov-Hebbian-learning clustering and illustrate its suitability in comparison to traditional methods by means of an artificial dataset and a real world example. |
| Further description: | Copyright ©2016 IEICE |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/4217 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | Life Sciences and Facility Management |
| Organisational Unit: | Institute of Computational Life Sciences (ICLS) |
| Appears in collections: | Publikationen Life Sciences und Facility Management |
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| nolta16_clusteredmds.pdf | 281.69 kB | Adobe PDF |  View/Open |
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Ott, T., Schüle, M., Held, J., Albert, C., & Stoop, R. (2016). Clustered multidimensional scaling with Rulkov neurons [Conference paper]. 2016 International Symposium on Nonlinear Theory and Its Applications, 389–392. https://doi.org/10.21256/zhaw-3532
Ott, T. et al. (2016) ‘Clustered multidimensional scaling with Rulkov neurons’, in 2016 International Symposium on Nonlinear Theory and Its Applications. IEICE, pp. 389–392. Available at: https://doi.org/10.21256/zhaw-3532.
T. Ott, M. Schüle, J. Held, C. Albert, and R. Stoop, “Clustered multidimensional scaling with Rulkov neurons,” in 2016 International Symposium on Nonlinear Theory and Its Applications, 2016, pp. 389–392. doi: 10.21256/zhaw-3532.
OTT, Thomas, Martin SCHÜLE, Jenny HELD, Carlo ALBERT und Ruedi STOOP, 2016. Clustered multidimensional scaling with Rulkov neurons. In: 2016 International Symposium on Nonlinear Theory and Its Applications. Conference paper. IEICE. 2016. S. 389–392
Ott, Thomas, Martin Schüle, Jenny Held, Carlo Albert, and Ruedi Stoop. 2016. “Clustered Multidimensional Scaling with Rulkov Neurons.” Conference paper. In 2016 International Symposium on Nonlinear Theory and Its Applications, 389–92. IEICE. https://doi.org/10.21256/zhaw-3532.
Ott, Thomas, et al. “Clustered Multidimensional Scaling with Rulkov Neurons.” 2016 International Symposium on Nonlinear Theory and Its Applications, IEICE, 2016, pp. 389–92, https://doi.org/10.21256/zhaw-3532.
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