Abstract
Self-organizing maps are closely related to radial basis function networks. They can be seen as radial basis function networks without an output layer, or, rather, the hidden layer of a radial basis function network is already the output layer of a self-organizing map. This output layer also has an internal structure since the neurons are arranged in a grid. The neighborhood relationships resulting from this grid are exploited in the training process in order to determine a topology preserving map.
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Notes
- 1.
Note that the distance is computed from the grid, in which the output neurons are arranged, and not from the position of the reference vectors or the distance measure in the input space.
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Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., Steinbrecher, M. (2016). Self-organizing Maps. In: Computational Intelligence. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-7296-3_7
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DOI: https://doi.org/10.1007/978-1-4471-7296-3_7
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