Learning in indefinite proximity spaces - Recent trends

Abstract

Efficient learning of a data analysis task strongly depends on the data representation. Many methods rely on symmetric similarity or dissimilarity representations by means of metric inner products or distances, providing easy access to powerful mathematical formalisms like kernel approaches. Similarities and dissimilarities are however often naturally obtained by non-metric proximity measures which can not easily be handled by classical learning algorithms. Major efforts have been undertaken to provide approaches which can either directly be used for such data or to make standard methods available for these type of data. We provide an overview about recent achievements in the field of learning with indefinite proximities.