Header

UZH-Logo

Maintenance Infos

Clustered Multidimensional Scaling with Rulkov Neurons


Ott, Thomas; Schuele, Martin; Held, Jenny; Albert, Carlo; Stoop, Ruedi (2016). Clustered Multidimensional Scaling with Rulkov Neurons. In: NOLTA 2016: International Symposium on Nonlinear Theory and Its Applications, Yugawara, 27 November 2016 - 30 November 2016.

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.

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.

Statistics

Downloads

5 downloads since deposited on 23 Feb 2018
5 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:30 November 2016
Deposited On:23 Feb 2018 10:09
Last Modified:31 Jul 2018 05:12
Publisher:Proceedings of the 2016 International Symposium on Nonlinear Theory and its Applications (NOLTA)
Series Name:Nolta Proceedings 2016
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:http://www.ieice.org/nolta/symposium/archive/2016/articles/1106.pdf

Download

Download PDF  'Clustered Multidimensional Scaling with Rulkov Neurons'.
Preview
Content: Published Version
Filetype: PDF
Size: 371kB