Pattern recognition with non-euclidean similarities

Abstract

Pairwise dissimilarity representations are frequently used as an alternative to feature vectors in pattern recognition.One of the problems encountered in the analysis of such data, is that the dissimilarities are rarely Euclidean, while statistical learning algorithms often rely on Euclidean distances. Such non-Euclidean dissimilarities are often corrected or imposed geometry via embedding. This talk reviews and and extends the field of analysing non-Euclidean dissimilarity data.