In this paper we shall utilize the concepts of Vector Quantization (VQ) for the computation of arbitrary distance functions - a problem which has been receiving much attention in the Operations Research and Location Analysis community. The input to our problem is the set of coordinates of a large number of nodes whose inter-node arbitrary 'distances' have to be estimated. Unlike traditional Operations Research methods, which use parametric functional estimators, we have utilized VQ principles to first adaptively polarize the nodes into sub-regions according to Kohonen's Self-Organizing Map (SOM). Subsequently, the parameters characterizing the sub-regions are learnt by using a variety of methods.

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Conference Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6)
Oommen, J, Altinel, I.Kuban (I. Kuban), & Aras, Necati (Necati). (1995). Arbitrary distance function estimation using vector quantization. In IEEE International Conference on Neural Networks - Conference Proceedings (pp. 3062–3067).