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, 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.
Learning Vector Quantization: Cluster Size and Cluster Number / Borgelt, C.; Girimonte, D.; Acciani, G.. - STAMPA. - (2004), pp. 808-811. (Intervento presentato al convegno IEEE International Symposium on Circuits and Systems, ISCAS 2004 tenutosi a Vancouver, Canada nel May 23-26, 2004) [10.1109/ISCAS.2004.1329931].
Learning Vector Quantization: Cluster Size and Cluster Number
G. Acciani
2004-01-01
Abstract
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, 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
