Comparison of Supervised Self-Organizing Maps Using Euclidian or Mahalanobis Distance in Classification Context

摘要

The supervised self-organizing map consists in associating output vectors to input vectors through a map, after self-organizing it on the basis of both input and desired output given altogether. This paper compares the use of Euclidian distance and Mahalanobis distance for this model. The distance comparison is made on a data classification application with either global approach or partitioning approach. The Mahalanobis distance in conjunction with the partitioning approach leads to interesting classification results.