Artefactual Structure from Least-Squares Multidimensional Scaling
Abstract
We consider the problem of illusory or artefactual structure from the vi- sualisation of high-dimensional structureless data. In particular we ex- amine 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 SS TRESS measure) gives rise to an annular structure when the input data is drawn from a high- dimensional isotropic distribution, and we provide a theoretical justifica- tion for this observation.
Cite
Text
Hughes and Lowe. "Artefactual Structure from Least-Squares Multidimensional Scaling." Neural Information Processing Systems, 2002.Markdown
[Hughes and Lowe. "Artefactual Structure from Least-Squares Multidimensional Scaling." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/hughes2002neurips-artefactual/)BibTeX
@inproceedings{hughes2002neurips-artefactual,
title = {{Artefactual Structure from Least-Squares Multidimensional Scaling}},
author = {Hughes, Nicholas P. and Lowe, David},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {937-944},
url = {https://mlanthology.org/neurips/2002/hughes2002neurips-artefactual/}
}