Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3532
Title: Clustered multidimensional scaling with Rulkov neurons
Authors : Ott, Thomas
Schüle, Martin
Held, Jenny
Albert, Carlo
Stoop, Ruedi
Proceedings: 2016 International Symposium on Nonlinear Theory and Its Applications
Pages : 389
Pages to: 392
Conference details: NOLTA2016, Yugawara, Japan, November 27th-30th, 2016
Publisher / Ed. Institution : IEICE
Issue Date: Nov-2016
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (Publication)
Language : English
Subjects : Clustering; Neural Network; Dimensionality Reduction
Subject (DDC) : 004: Computer science
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
Departement: Life Sciences und Facility Management
Organisational Unit: Institute of Applied Simulation (IAS)
Publication type: Conference Paper
DOI : 10.21256/zhaw-3532
URI: https://digitalcollection.zhaw.ch/handle/11475/4217
Appears in Collections:Publikationen Life Sciences und Facility Management

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