TurSOM: a turing inspired self-organizing map
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Growing mechanisms and cluster identification with TurSOM
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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Initialization of self-organizing maps is typically based on random vectors within the given input space. The implicit problem with random initialization is the overlap (entanglement) of connections between neurons. In this paper, we present a new method of initialization based on a set of self-similar curves known as Hilbert curves. Hilbert curves can be scaled in network size for the number of neurons based on a simple recursive (fractal) technique, implicit in the properties of Hilbert curves. We have shown that when using Hilbert curve vector (HCV) initialization in both classical SOM algorithm and in a parallel-growing algorithm (ParaSOM), the neural network reaches better coverage and faster organization.