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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Generating low-dimensional denoised embeddings of nonlinear data with superparamagentic agents
Organization Unit
  • Thomas Ott
  • Thomas Eggel
  • Markus Christen
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Page Range 180 - 183
Event Title Nonlinear Theory and its Applications
Event Type conference
Event Location Luzern
Event Start Date September 14 - 2014
Event End Date September 18 - 2014
Publisher s.n.
Abstract Text 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.
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Additional Information Proceedings of Nonlinear Theory and Applications