Learning more accurate metrics for Self-Organizing Maps

Abstract

Improved methods are presented for learning metrics that measure only important distances. It is assumed that changes in primary data are relevant only to the extent that they cause changes in auxiliary data, available paired with the primary data. The metrics are here derived from estimators of the conditional density of the auxiliary data. More accurate estimators are compared, and a more accurate approximation to the distances is introduced. The new methods improved the quality of Self-Organizing Maps (SOMs) significantly for four of the five studied data sets. © Springer-Verlag Berlin Heidelberg 2002.