Magnification Control in Relational Neural Gas
نویسندگان
چکیده
Prototype-based clustering algorithms such as the Self Organizing Map (SOM) or Neural Gas (NG) offer powerful tools for automated data inspection. The distribution of prototypes, however, does not coincide with the underlying data distribution and magnification control is necessary to obtain information theoretic optimum maps. Recently, several extensions of SOM and NG to general non-vectorial dissimilarity data have been proposed, such as Relational NG (RNG). Here, we derive a magnification control scheme for RNG based on localized learning, and we demonstrate its applicability for various data sets.
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