On learning asymmetric dissimilarity measures

Many practical applications require that distance measures to be asymmetric and context-sensitive. We introduce context-sensitive learnable asymmetric dissimilarity (CLAD) measures, which are defined to be a weighted sum of a fixed number of dissimilarity measures where the associated weights depend on the point from which the dissimilarity is measured. The parameters used in defining the measure capture the global relationships among the features. We provide an algorithm to learn the dissimilarity measure automatically from a set of user specified comparisons in the form "x is closer to y than to z" and study its performance. The experimental results show that the proposed algorithm outperforms other approaches due to the context sensitive nature of the CLAD measures.