Self Organizing Map (SOM) has been applied into several classical modeling tasks including clustering, classification, function approximation and visualization of high dimensional spaces. The final products of a trained SOM are a set of ordered (low dimensional) indices and their associated high dimensional weight vectors. While in the above-mentioned applications, the final high dimensional weight vectors play the primary role in the computational steps, from a certain perspective, one can

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... rpret SOM as a nonparametric encoder, in which the final low dimensional indices of the trained SOM are pointer to the high dimensional space. We showed how using a one-dimensional SOM, which is not common in usual applications of SOM, one can develop a nonparametric mapping from a high dimensional space to a continuous one-dimensional numerical field. These numerical values, called contextual numbers, are ordered in a way that in a given context, similar numbers refer to similar high dimensional states. Further, as these numbers can be treated similarly to usual continuous numbers, they can be replaced with their corresponding high dimensional states within any data driven modeling problem. As a potential application, we showed how using contextual numbers could be used for the problem of high dimensional spatiotemporal dynamics.