Parallelization in combining the SOM and Sammon's mapping
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....
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Vilnius University Press
2003-12-01
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| Series: | Lietuvos Matematikos Rinkinys |
| Online Access: | https://www.journals.vu.lt/LMR/article/view/32403 |
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| Summary: | 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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| ISSN: | 0132-2818 2335-898X |