Improved data visualisation through nonlinear dissimilarity modelling

Research output: Contribution to journalArticle

View graph of relations Save citation

Authors

Research units

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.

Request a copy

Request a copy

Documents

  • Improved data visualisation through nonlinear dissimilarity modelling

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

    Accepted author manuscript, 7 MB, PDF-document

    Embargo ends: 1/08/18

    License: CC BY-NC-ND Show license

Details

Original languageEnglish
JournalPattern Recognition
Volumein press
Early online date2 Aug 2017
DOIs
StateE-pub ahead of print - 2 Aug 2017

Bibliographic 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

DOI

Employable Graduates; Exploitable Research

Copy the text from this field...