Relational discriminant analysis

Relational discriminant analysis is based on a proximity description of the data. Instead of features, the similarities to a subset of the objects in the training data are used for representation. In this paper we will show that this subset might be small and that its exact choice is of minor importance. Moreover, it is shown that linear or non-linear methods for feature extraction based on multi-dimensional scaling are not, or just hardly better than subsets. Selection drastically simplifies the problem of dimension reduction. Relational discriminant analysis may thus be a valuable pattern recognition tool for applications in which the choice of the features is uncertain.