Improved data visualisation through nonlinear dissimilarity modelling

Iain Rice

Research output: Contribution to journalArticle

Abstract

Inherent to state-of-the-art dimension reduction algorithms is the assumption that global distances between observations are Euclidean, despite the potential for altogether non-Euclidean data manifolds. We demonstrate that a non-Euclidean manifold chart can be approximated by implementing a universal approximator over a dictionary of dissimilarity measures, building on recent developments in the field. This approach is transferable across domains such that observations can be vectors, distributions, graphs and time series for instance. Our novel dissimilarity learning method is illustrated with four standard visualisation datasets showing the benefits over the linear dissimilarity learning approach.
Original languageEnglish
JournalPattern Recognition
Volumein press
Early online date2 Aug 2017
DOIs
Publication statusE-pub ahead of print - 2 Aug 2017

Bibliographical note

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • dissimilarity
  • multidimensional scaling
  • visualisation
  • RBF network

Fingerprint Dive into the research topics of 'Improved data visualisation through nonlinear dissimilarity modelling'. Together they form a unique fingerprint.

  • Cite this