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Retraining the Neural Network for Data Visualization
IFIP International Federation for Information Processing
In this paper, we discuss the visuaHzation of multidimensional data. A well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one is Sammon's mapping. The algorithm is oriented to minimize the projection error. We investigate an unsupervised backpropagation algorithm to train a multilayer feed-forward neural network (SAMANN) to perform the Sammon's nonlinear projection. Sammon mapping has a disadvantage. It lacks generalization, which means that new pointsdoi:10.1007/0-387-34224-9_4 dblp:conf/ifip12/MedvedevD06 fatcat:725ile6d2jcmfn5hf4v6vwjxkq