Abstract
Sammon’s mapping is a well-known procedure for mapping data from a higher-dimensional space onto a lower-dimensional one. The original algorithm has a disadvantage. It lacks generalization, which means that new points cannot be added to the obtained map without recalculating it. SAMANN neural network, that realizes Sammon’s algorithm, provides a generalization capability of projecting new data. Speed up of the SAMANN network retraining when the new data points appear has been analyzed in this paper. Two strategies for retraining the neural network that realizes the multidimensional data visualization have been proposed and then the analysis has been made.
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Medvedev, V., Dzemyda, G. (2006). Speed Up of the SAMANN Neural Network Retraining. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_11
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DOI: https://doi.org/10.1007/11785231_11
Publisher Name: Springer, Berlin, Heidelberg
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