ekalska. Non-Euclidean dissimilarities: Causes and informativeness

Abstract. In the process of designing pattern recognition systems one may choose a representation based on pairwise dissimilarities between ob-jects. This is especially appealing when a set of discriminative features is difficult to find. Various classification systems have been studied for such a dissimilarity representation: the direct use of the nearest neighbor rule, the postulation of a dissimilarity space and an embedding to a virtual, underlying feature vector space. It appears in several applications that the dissimilarity measures con-structed by experts tend to have a non-Euclidean behavior. In this paper we first analyze the causes of such choices and then experimentally verify that the non-Euclidean property of the measure can be informative1.