Abstract
Application-specific dissimilarity functions can be used for learning from a set of objects represented by pairwise dissimilarity matrices in this context. These dissimilarities may, however, suffer from various defects, e.g. when derived from a suboptimal optimization or by the use of non-metric or noisy measures. In this paper, we study procedures for refining such dissimilarities. These methods work in a representation space, either a dissimilarity space or a pseudo-Euclidean embedded space. On a series of experiments we show that refining may significantly improve the nearest neighbor classifications of dissimilarity measurements.