Using Dominant Sets for k-NN Prototype Selection

k-Nearest Neighbors is surely one of the most important and widely adopted non-parametric classification methods in pattern recognition. It has evolved in several aspects in the last 50 years, and one of the most known variants consists in the usage of prototypes: a prototype distills a group of similar training points, diminishing drastically the number of comparisons needed for the classification; actually, prototypes are employed in the case the cardinality of the training data is high. In this paper, by using the dominant set clustering framework, we propose four novel strategies for the prototype generation, allowing to produce representative prototypes that mirror the underlying class structure in an expressive and effective way. Our strategy boosts the k-NN classification performance; considering heterogeneous metrics and analyzing 15 diverse datasets, we are among the best 6 prototype-based k-NN approaches, with a computational cost which is strongly inferior to all the competitors. In addition, we show that our proposal beats linear SVM in the case of a pedestrian detection scenario.