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.
MV is a Senior research associate at the Belgian FNRS. GS is funded by the Belgian FRIA. The work of DF and VW is supported by the Interuniversity Attraction Pole (IAP), initiated by the Belgian Federal State, Ministry of Sciences, Technologies and Culture. The scientific responsibility rests with the authors.
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Verleysen, M., Francois, D., Simon, G., Wertz, V. (2003). On the effects of dimensionality on data analysis with neural networks. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_14
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DOI: https://doi.org/10.1007/3-540-44869-1_14
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