Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The Self-organizing map as a tool in knowledge engineering
Pattern recognition in soft computing paradigm
Self-Organizing Maps
A Novel Measure for Quantifying the Topology Preservation of Self-Organizing Feature Maps
Neural Processing Letters
New Developments and Applications of Self-Organizing Maps
NICROSP '96 Proceedings of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (NICROSP '96)
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Input feature selection for classification problems
IEEE Transactions on Neural Networks
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Kohonen's Self-organizing Map (SOM) is one of the most popular neural network algorithms. SOM produces topology preserving map of the input data. In the current study the SOM's topology preservation property is used to identify the input features whose removal does not affect significantly the neighborhood relations among the input data points. The topology preservation property of of an SOM is measured using a quantitative index. However the same index can be slightly modified to compute topology preservation in the SOM along individual features. Thus studying the topology preservation due to each individual feature we can compare their quality with respect to their importance in affecting the neighborhood relation among input points. Experimental study is conducted with a synthetic data set, well known Iris data set and a multi-channel satellite image dataset. The results are cross verified by comparing with Sammon error of the data computed in the corresponding dimension. k-NN classification performance is also considered for the data sets.