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
Optimization of the Local Search in the Training for SAMANN Neural Network
Journal of Global Optimization
Scientific Parallel Computing
Parallel Programming in C with MPI and OpenMP
Parallel Programming in C with MPI and OpenMP
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
Large Datasets Visualization with Neural Network Using Clustered Training Data
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
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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. But the original algorithm has a disadvantage. It lacks generalization, which means that new points cannot be added to the obtained map without recalculating it. The SAMANN neural network, that realizes Sammon's algorithm, provides a generalization capability of projecting new data. A drawback of using SAMANN is that the training process is extremely slow. One of the ways of speeding up the neural network training process is to use parallel computing. In this paper, we proposed some parallel realizations of the SAMANN.