Retraining of the SAMANN Network Viktor Medvedev Viktor Medvedev, Gintautas Dzemyda {Viktor.m, Institute of Mathematics and Informatics Vilnius, Lithuania 32nd International Conference on Current Trends in Theory and Practice of Computer Science Student Research Forum January , 2006 Merin, Czech Republic SOFSEM 2006, SRF

Multidimensional data. Observations from real-world problems are often highdimensional vectors. The problem is to discover knowledge in the set of multidimensional points. Visualization is a powerful tool in data analysis. It makes easier the understandability and perception of data. Sammon’s mapping, multidimensional scaling, principal components Sammon‘s mapping – a well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one. A neural network for sammon’s projection Key words: visualization, multidimensional data, Sammon’s mapping, SAMANN neural network, retraining of the network SOFSEM 2006, SRF

SAMANN – a specific backpropagation algorithm to train a multilayer feed-forward artificial neural network (SAMANN) to perform the Sammon‘s nonlinear projection in an unsupervised way. The network is able to project new patterns after training. Retraining of the network. While working with large data amounts there may appear a lot of new vectors. Strategies for retraining the network. Some strategies for retraining the network that realizes multidimensional data visualization have been proposed. One of the proposed strategies enables us to attain good visualization results in a very short time as well as to get smaller visualization errors and to improve the accuracy of projection as compared to other strategies. SOFSEM 2006, SRF

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