Abstract
We present a novel self-organizing network which is generated by a growth process. The application range of the model is the same as for Kohonen’s feature map: generation of topology-preserving and dimensionality-reducing mappings, e.g., for the purpose of data visualization. The network structure is a rectangular grid which, however, increases its size during self-organization. By inserting complete rows or columns of units the grid may adapt its height/width ratio to the given pattern distribution. Both the neighborhood range used to co-adapt units in the vicinity of the winning unit and the adaptation strength are constant during the growth phase. This makes it possible to let the network grow until an application-specific performance criterion is fulfilled or until a desired network size is reached. A final approximation phase with decaying adaptation strength finetunes the network.
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Fritzke, B. Growing Grid — a self-organizing network with constant neighborhood range and adaptation strength. Neural Process Lett 2, 9–13 (1995). https://doi.org/10.1007/BF02332159
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DOI: https://doi.org/10.1007/BF02332159