Title: Visualization of multimedimensional data taking into account the learning flow of the self-organizing neural network
Authors: Dzemyda, Gintautas
Kurasova, Olga
Citation: Journal of WSCG. 2003, vol. 11, no. 1-3.
Issue Date: 2003
Publisher: UNION Agency – Science Press
Document type: článek
URI: http://wscg.zcu.cz/wscg2003/Papers_2003/C11.pdf
ISSN: 1213-6972
Keywords: vizualizace dat;neuronové sítě;samoorganizující se mapy
Keywords in different language: data visualization;neural networks;self-organizing maps
Abstract: In the paper, we discuss the visualization of multidimensional vectors taking into account the learning flow of the self-organizing neural network. A new algorithm realizing a combination of the self-organizing map (SOM) and Sammon’s mapping has been proposed. It takes into account the intermediate learning results of the SOM. The experiments have showed that the algorithm gives lower mean projection errors as compared with a consequent application of the SOM and Sammon’s mapping. This is the essential advantage of the new algorithm, i.e. we succeed to eliminate the influence of the “magic factor” a ( 0 <a £1 ) on Sammon’s mapping results. For larger values of a (a >1 ), the mean projection error grows. However, in this case the new algorithm operates more stable and gives smaller values of the mean projection error.
Rights: © UNION Agency – Science Press
Appears in Collections:Volume 11, number 1-3 (2003)

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/1629

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