Abstract
Neural maps are a very popular class of unsupervised neural networks that project high-dimensional data of the input space onto a neuron position in a low-dimensional output space grid. It is desirable that the projection effectively preserves the structure of the data.
In this paper we present a hybrid model called K-Dynamical Self Organizing Maps (KDSOM) consisting of K Self Organizing Maps with the capability of growing and interacting with each other. The input space is soft partitioned by the lattice maps. The KDSOM automatically finds its structure and learns the topology of the input space clusters.
We apply our KDSOM model to three examples, two of which involve real world data obtained from a site containing benchmark data sets.
This work was supported in part by Research Grant Fondecyt 1040365, DGIP-UTFSM, BMBF-CHL 03-Z13 from German Ministry of Education, DIPUV-22/2004 and CID-04/2003.
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Saavedra, C., Allende, H., Moreno, S., Salas, R. (2005). K-Dynamical Self Organizing Maps. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_71
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DOI: https://doi.org/10.1007/11579427_71
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