Algorithms for clustering data
Algorithms for clustering data
Sammon's mapping using neural networks: a comparison
Pattern Recognition Letters - special issue on pattern recognition in practice V
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visualization of a set of parameters characterized by their correlation matrix
Computational Statistics & Data Analysis
Self-Organizing Maps
Optimization of the Local Search in the Training for SAMANN Neural Network
Journal of Global Optimization
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
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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.