On the effects of dimensionality on data analysis with neural networks

Authors:
M. Verleysen;D. Francois;G. Simon;V. Wertz
Affiliations:
DICE, Université catholique de Louvain, Louvain-la-Neuve, Belgium B-1348;CESAME, Université catholique de Louvain, Louvain-la-Neuve, Belgium B-1348;DICE, Université catholique de Louvain, Louvain-la-Neuve, Belgium B-1348;CESAME, Université catholique de Louvain, Louvain-la-Neuve, Belgium B-1348
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

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Abstract

Modern data analysis often faces high-dimensional data. Nevertheless, most neural network data analysis tools are not adapted to high-dimensional spaces, because of the use of conventional concepts (as the Euclidean distance) that scale poorly with dimension. This paper shows some limitations of such concepts and suggests some research directions as the use of alternative distance definitions and of non-linear dimension reduction.