Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3565
Title: Generating low-dimensional denoised representations of nonlinear data with superparamagnetic agents
Authors : Eggel, Thomas
Christen, Markus
Ott, Thomas
Proceedings: Proceedings of the 2014 International Symposium on Nonlinear Theory and its Applications (NOLTA2014)
Pages : 180
Pages to: 183
Conference details: NOLTA2014, Luzern, Switzerland, September 14-18 2014
Publisher / Ed. Institution : IEICE
Issue Date: 2014
Language : Englisch / English
Subjects : Clustering; Dimensionality; Reduction
Subject (DDC) : 500: Naturwissenschaften und Mathematik
Abstract: Visualisation of high-dimensional data by means of a low-dimensional embedding plays a key role in explorative data analysis. Classical approaches to dimensionality reduction, such as principal component analysis (PCA) and multidimensional scaling (MDS), struggle or even fail to reveal the relevant data characteristics when applied to noisy or nonlinear data structures. We present a novel approach for dimensionality reduction in combination with an automatic noise cleaning. By employing self-organising agents that are governed by the dynamics of the superparamagnetic clustering algorithm, the method is able to generate denoised low-dimensional embeddings for which the characteristics of nonlinear data structures are preserved or even emphasised. These properties are illustrated and compared to other approaches by means of toy and real-world examples.
Further description : Copyright ©2016 IEICE
Departement: Life Sciences und Facility Management
Organisational Unit: Institut für Angewandte Simulation (IAS)
Publication type: Konferenz: Paper / Conference Paper
Type of review: Peer review (Publikation)
DOI : 10.21256/zhaw-3565
URI: https://digitalcollection.zhaw.ch/handle/11475/4396
License (according to publishing contract) : Lizenz gemäss Verlagsvertrag / Licence according to publishing contract
Appears in Collections:Publikationen Life Sciences und Facility Management

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