Artefactual structure from least squares multidimensional scaling

Nicholas P. Hughes, David Lowe

    Research output: Chapter in Book/Published conference outputConference publication


    We consider the problem of illusory or artefactual structure from the visualisation of high-dimensional structureless data. In particular we examine the role of the distance metric in the use of topographic mappings based on the statistical field of multidimensional scaling. We show that the use of a squared Euclidean metric (i.e. the SSTRESs measure) gives rise to an annular structure when the input data is drawn from a high-dimensional isotropic distribution, and we provide a theoretical justification for this observation.

    Original languageEnglish
    Title of host publicationAdvances in Neural Information Processing Systems
    EditorsS. Becker, S. Thrun, K. Obermeyer
    Number of pages8
    Publication statusPublished - 2003
    Event16th Annual Neural Information Processing Systems Conference, NIPS 2002 - Vancouver, BC, United Kingdom
    Duration: 9 Dec 200214 Dec 2002


    Conference16th Annual Neural Information Processing Systems Conference, NIPS 2002
    Country/TerritoryUnited Kingdom
    CityVancouver, BC


    • Problem of illusory
    • artefactual structure
    • visualisation
    • high-dimensional structureless data
    • topographic mappings
    • squared Euclidean metric
    • high-dimensional isotropic distribution


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