This project contains a Java implementation of median relational generalized learning vector
quantization as proposed by Nebel, Hammer, Frohberg, and Villmann
(2015, doi:10.1016/j.neucom.2014.12.096). Given a matrix of pairwise distances D and a
vector of labels Y it identifies prototypical data points (i.e. rows of D) which help
to classify the data set using a simple nearest neighbor rule. In particular, the algorithm
optimizes the generalized learning vector quantization cost function (Sato and Yamada, 1995)
via an expectation maximization scheme where in each iteration one prototype 'jumps' to
another data point in order to improve the cost function. If the cost function can not be
improved anymore for any of the data points, the algorithm terminates.