We propose a method to determine the relevance of the different input dimensions for a self organizing map (SOM). First, a growing self organizing map is adapted to the data. Afterwards, the effect of the input dimensions on the clustering or the topology of the SOM, respectively, is computed and the data dimensions which are ranked low are pruned. The algorithm is applied to real life satellite image data. The results are verified via visualizing the data in RGB-images as well as explicitely computing the classification error.
展开▼
