Relational Neural Gas

Barbara Hammer, Alexander Hasenfuss

Abstract: We introduce relational variants of neural gas, a very efficient and powerful neural clustering algorithm, which allow a clustering and mining of data given in terms of a pairwise similarity or dissimilarity matrix. It is assumed that this matrix stems from Euclidean distance or dot product, respectively, however, the underlying embedding of points is unknown. One can equivalently formulate batch optimization in terms of the given similarities or dissimilarities, thus providing a way to transfer batch optimization to relational data. For this procedure, convergence is guaranteed and extensions such as the integration of label information can readily be transferred to this framework.