Learning vector quantization: cluster size and cluster number

C. Borgelt, D. Girimonte, G. Acciani
2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)  
We study learning vector quantization methods to adapt the size of (hyper-)spherical clusters to better fit a given data set, especially in the context of non-normalized activations. The basic idea of our approach is to compute a desired radius from the data points that are assigned to a cluster and then to adapt the current radius of the cluster in the direction of this desired radius. Since cluster size adaptation has a considerable impact on the number of clusters needed to cover a data set,
more » ... o cover a data set, we also examine how to select the number of clusters based on validity measures and, in the context of non-normalized activations, on the coverage of the data.
doi:10.1109/iscas.2004.1329931 fatcat:vbgnszvgmjdlzezp6i3y7a56v4