Abstract
This paper presents a generalized framework of a self-organizing map (SOM) applicable to more extended data classes rather than vector data. A modular structure is adopted to realize such generalization; thus, it is called a modular network SOM (mnSOM), in which each reference vector unit of a conventional SOM is replaced by a functional module. Since users can choose the functional module from any trainable architecture such as neural networks, the mnSOM has a lot of flexibility as well as high data processing ability. In this paper, the essential idea is first introduced and then its theory is described.
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Furukawa, T., Tokunaga, K. (2006). Generalization of the Self-Organizing Map: From Artificial Neural Networks to Artificial Cortexes. 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_105
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DOI: https://doi.org/10.1007/11893028_105
Publisher Name: Springer, Berlin, Heidelberg
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