We show that neural networks, with a suitable error function for back- propagation, can be successfully used for metric multidimensional scal- ing (MDS) (i.e., dimensional reduction while trying to preserve the origi- nal distances between patterns) and are in fact able to outdo the standard algebraic approach to MDS, known as classical scaling.
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