Abstract
In this paper we present a neural network for nonmetric multidimensional scaling. In our approach, the monotone transformation that is a part of every nonmetric scaling algorithm is performed by a special feedforward neural network with a modified backpropagation algorithm. Contrary to traditional methods, we thus explicitly model the monotone transformation by a special purpose neural network. The architecture of the new network and the derivation of the learning rule are given, as well as some experimental results. The experimental results are positive.
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van Wezel, M.C., Kosters, W.A., van der Putten, P., Kok, J.N. (2001). Nonmetric Multidimensional Scaling with Neural Networks. In: Hoffmann, F., Hand, D.J., Adams, N., Fisher, D., Guimaraes, G. (eds) Advances in Intelligent Data Analysis. IDA 2001. Lecture Notes in Computer Science, vol 2189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44816-0_15
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DOI: https://doi.org/10.1007/3-540-44816-0_15
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