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- W2247455993 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." @default.
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- W2247455993 date "2003-01-01" @default.
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- W2247455993 title "On the effects of dimensionality on data analysis with neural networks" @default.
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- W2247455993 doi "https://doi.org/10.1007/3-540-44869-1_14" @default.
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