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This research aims to develop kernel GNG, a kernelized version of the growingneural gas (GNG) algorithm, and to investigate the features of the networksgenerated by the kernel GNG. The GNG is an unsupervised artificial neuralnetwork that can transform a dataset into an undirected graph, therebyextracting the features of the dataset as a graph. The GNG is widely used invector quantization, clustering, and 3D graphics. Kernel methods are often usedto map a dataset to feature space, with support vector machines being the mostprominent application. This paper introduces the kernel GNG approach andexplores the characteristics of the networks generated by kernel GNG. Fivekernels, including Gaussian, Laplacian, Cauchy, inverse multiquadric, and logkernels, are used in this study. The results of this study show that theaverage degree and the average clustering coefficient decrease as the kernelparameter increases for Gaussian, Laplacian, Cauchy, and IMQ kernels. If weavoid more edges and a higher clustering coefficient (or more triangles), thekernel GNG with a larger value of the parameter will be more appropriate.

Characteristics of networks generated by kernel growing neural gas