White box classification of dissimilarity data

Abstract

While state-of-the-art classifiers such as support vector machines offer efficient classification for kernel data, they suffer from two drawbacks: the underlying classifier acts as a black box which can hardly be inspected by humans, and non-positive definite Gram matrices require additional preprocessing steps to arrive at a valid kernel. In this approach, we extend prototype-based classification towards general dissimilarity data resulting in a technology which (i) can deal with dissimilarity data characterized by an arbitrary symmetric dissimilarity matrix, (ii) offers intuitive classification in terms of prototypical class representatives, and (iii) leads to state-of-the-art classification results. © 2012 Springer-Verlag.