NeuroScale: Novel Topographic Feature Extraction using RBF Networks

Dimension-reducing feature extraction neural network techniques which also preserve neighbourhood relationships in data have tra(cid:173) ditionally been the exclusive domain of Kohonen self organising maps. Recently, we introduced a novel dimension-reducing feature extraction process, which is also topographic, based upon a Radial Basis Function architecture. It has been observed that the gener(cid:173) alisation performance of the system is broadly insensitive to model order complexity and other smoothing factors such as the kernel widths, contrary to intuition derived from supervised neural net(cid:173) work models. In this paper we provide an effective demonstration of this property and give a theoretical justification for the apparent'self-regularising' behaviour of the'NEUROSCALE' architecture. Recently an important class of topographic neural network based feature extraction approaches, which can be related to the traditional statistical methods of Sammon Mappings (Sammon, 1969) and Multidimensional Scaling (Kruskal, 1964), have been introduced (Mao and Jain, 1995; Lowe, 1993; Webb, 1995; Lowe and Tipping, 1996).