Metric learning in dissimilarity space for improved nearest neighbor performance

Abstract

Showing the nearest neighbor is a useful explanation for the result of an automatic classification. Given, expert defined, distance measures may be improved on the basis of a training set. We study several proposals to optimize such measures for nearest neighbor classification, explicitly including non-Euclidean measures. Some of them may directly improve the distance measure, others may construct a dissimilarity space for which the Euclidean distances show significantly better performances. Results are application dependent and raise the question what characteristics of the original distance measures influence the possibilities of metric learning. © 2014 Springer-Verlag Berlin Heidelberg.