On the Effects of Dimensionality on Data Analysis with Neural Networks
نویسندگان
چکیده
Modern data analysis often faces high-dimensional data. Nevertheless, most neural network data analysis tools are not adapted to highdimensional 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.
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تاریخ انتشار 2003