Abstract
In this paper, we present an extended self-organizing map. It keeps full connectivity between adjacent layers but adds new virtual connections between neurons of competitive layer so that the structure of competitive layer can be regarded as a graph and can be expressed by an adjacent matrix. Thus, the conventional SOMs can be regarded as special cases of the extended model. Then we can evolve the graph into arbitrary topology such as small world graph and random graph. After evolution we can obtain arbitrary nonlinear neighborhood kernel of neurons and the obtained topology of competitive layer is expected to simulate the distribution of input samples. The experimental results show that the new extended model has better performance in speed and self-organization than conventional ones.
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Yang, S., Luo, S., Li, J. (2006). An Extended Model on Self-Organizing Map. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_110
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DOI: https://doi.org/10.1007/11893028_110
Publisher Name: Springer, Berlin, Heidelberg
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