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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3532
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dc.contributor.authorOtt, Thomas-
dc.contributor.authorSchüle, Martin-
dc.contributor.authorHeld, Jenny-
dc.contributor.authorAlbert, Carlo-
dc.contributor.authorStoop, Ruedi-
dc.date.accessioned2018-03-23T14:13:23Z-
dc.date.available2018-03-23T14:13:23Z-
dc.date.issued2016-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/4217-
dc.descriptionCopyright ©2016 IEICEde_CH
dc.description.abstractWhen 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.de_CH
dc.language.isoende_CH
dc.publisherIEICEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectClusteringde_CH
dc.subjectNeural Networkde_CH
dc.subjectDimensionality Reductionde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleClustered multidimensional scaling with Rulkov neuronsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.21256/zhaw-3532-
zhaw.conference.detailsNonlinear Theory and Applications 2016 (NOLTA), Yugawara, Japan, 27-30 November 2016de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end392de_CH
zhaw.pages.start389de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedings2016 International Symposium on Nonlinear Theory and Its Applicationsde_CH
zhaw.webfeedBio-Inspired Methods & Neuromorphic Computingde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedDigital Environment & Sustainabilityde_CH
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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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