Parallelization in combining the SOM and Sammon's mapping
Articles
Gintautas Dzemyda
Institute of Mathematics and Informatics
Olga Kurasova
Institute of Mathematics and Informatics
Virginijus Marcinkevičius
Institute of Mathematics and Informatics
Published 2003-12-22
https://doi.org/10.15388/LMR.2003.32403
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How to Cite

Dzemyda, G., Kurasova, O. and Marcinkevičius, V. (2003) “Parallelization in combining the SOM and Sammon’s mapping”, Lietuvos matematikos rinkinys, 43(spec.), pp. 218–222. doi:10.15388/LMR.2003.32403.

Abstract

In this paper, we propose a parallel algorithm for multidimensional data visualization combining the neural network (the self-organizing map-SOM) and Sammon’s mapping. Here n-dimensional vectors are projected onto the plane by using Sammon’s mapping taking into account the learning flow of the SOM. It is necessary to investigate some important factors that influence the efficiency of the parallel algorithm. The results of investigation allow us to optimize the number of the SOM training epochs, the number of the SOM training blocks, and the number of Sammon’s iterations.

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