Artefactual structure from least squares multidimensional scaling

Hughes, Nicholas P. and Lowe, David (2003). Artefactual structure from least squares multidimensional scaling. IN: Advances in Neural Information Processing Systems. Becker, S.; Thrun, S. and Obermeyer, K. (eds) Neural information processing systems foundation.

Abstract

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.

Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Event Title: 16th Annual Neural Information Processing Systems Conference, NIPS 2002
Event Type: Other
Event Dates: 2002-12-09 - 2002-12-14
Uncontrolled Keywords: Problem of illusory,artefactual structure,visualisation,high-dimensional structureless data,topographic mappings,squared Euclidean metric,high-dimensional isotropic distribution,Computer Networks and Communications,Information Systems,Signal Processing
Full Text Link: http://books.ni ... nips15/AA55.pdf
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2003
Authors: Hughes, Nicholas P.
Lowe, David

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