Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3565
Publication type: Conference paper
Type of review: Peer review (publication)
Title: Generating low-dimensional denoised representations of nonlinear data with superparamagnetic agents
Authors: Eggel, Thomas
Christen, Markus
Ott, Thomas
DOI: 10.21256/zhaw-3565
Proceedings: Proceedings of the 2014 International Symposium on Nonlinear Theory and its Applications (NOLTA2014)
Page(s): 180
Pages to: 183
Conference details: Nonlinear Theory and Applications 2014 (NOLTA), Luzern, 14-18 September 2014
Issue Date: 2014
Publisher / Ed. Institution: IEICE
Language: English
Subjects: Clustering; Dimensionality; Reduction
Subject (DDC): 510: Mathematics
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
URI: https://digitalcollection.zhaw.ch/handle/11475/4396
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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