Piecewise Linear Projection Based on Self-Organizing Map
Neural Processing Letters
Visual exploration of production data using small multiples design with non-uniform color mapping
Computers and Industrial Engineering
Complex Process Visualization through Continuous Feature Maps Using Radial Basis Functions
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Expanding self-organizing map for data visualization and cluster analysis
Information Sciences: an International Journal - Special issue: Soft computing data mining
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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An approach to dimension-reduction mapping of multidimensional pattern data is presented. The motivation for this work is to provide a computationally efficient method for visualizing large bodies of complex multidimensional data as a relatively “topologically correct” lower dimensional approximation. Examples of the use of this approach in obtaining meaningful two-dimensional (2-D) maps and comparisons with those obtained by the self-organizing map (SOM) and the neural-net implementation of Sammon's approach are also presented and discussed. In this method, the mapping equalizes and orthogonalizes the lower dimensional outputs by reducing the covariance matrix of the outputs to the form of a constant times the identity matrix. This new method is computationally efficient and “topologically correct” in interesting and useful ways