  This research aims to develop kernel GNG, a kernelized version of the growing
neural gas (GNG) algorithm, and to investigate the features of the networks
generated by the kernel GNG. The GNG is an unsupervised artificial neural
network that can transform a dataset into an undirected graph, thereby
extracting the features of the dataset as a graph. The GNG is widely used in
vector quantization, clustering, and 3D graphics. Kernel methods are often used
to map a dataset to feature space, with support vector machines being the most
prominent application. This paper introduces the kernel GNG approach and
explores the characteristics of the networks generated by kernel GNG. Five
kernels, including Gaussian, Laplacian, Cauchy, inverse multiquadric, and log
kernels, are used in this study. The results of this study show that the
average degree and the average clustering coefficient decrease as the kernel
parameter increases for Gaussian, Laplacian, Cauchy, and IMQ kernels. If we
avoid more edges and a higher clustering coefficient (or more triangles), the
kernel GNG with a larger value of the parameter will be more appropriate.
