Artefactual structure from least squares multidimensional scaling

Nicholas P. Hughes; David Lowe

Artefactual structure from least squares multidimensional scaling

Nicholas P. Hughes, David Lowe

Research output: Chapter in Book/Published conference output › Conference publication

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.

Original language	English
Title of host publication	Advances in Neural Information Processing Systems
Editors	S. Becker, S. Thrun, K. Obermeyer
Number of pages	8
Publication status	Published - 2003
Event	16th Annual Neural Information Processing Systems Conference, NIPS 2002 - Vancouver, BC, United Kingdom Duration: 9 Dec 2002 → 14 Dec 2002

Conference

Conference	16th Annual Neural Information Processing Systems Conference, NIPS 2002
Country/Territory	United Kingdom
City	Vancouver, BC
Period	9/12/02 → 14/12/02

Keywords

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

Access to Document

NCRG_2003_012.pdfAccepted author manuscript, 433 KB

Cite this

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title = "Artefactual structure from least squares multidimensional scaling",

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.",

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AU - Lowe, David

PY - 2003

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N2 - 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.

AB - 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.

KW - Problem of illusory

KW - artefactual structure

KW - visualisation

KW - high-dimensional structureless data

KW - topographic mappings

KW - squared Euclidean metric

KW - high-dimensional isotropic distribution

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UR - http://books.nips.cc/papers/files/nips15/AA55.pdf

M3 - Conference publication

SN - 0262025507

SN - 9780262025508

BT - Advances in Neural Information Processing Systems

A2 - Becker, S.

A2 - Thrun, S.

A2 - Obermeyer, K.

T2 - 16th Annual Neural Information Processing Systems Conference, NIPS 2002

Y2 - 9 December 2002 through 14 December 2002

ER -

Artefactual structure from least squares multidimensional scaling

Abstract

Conference

Keywords

Access to Document

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