# Characteristics of networks generated by kernel growing neural gas

arXiv: https://arxiv.org/abs/2308.08163 · HTML: https://mlsanity.com/paper/2308.08163/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 38 references that are in the map.
- First posted on arXiv 16 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?](https://mlsanity.com/paper/1407.0611/) (2014)
- [Approximate spectral clustering using both reference vectors and topology of the network generated by growing neural gas](https://mlsanity.com/paper/2009.07101/) (2020)
- [Extract an essential skeleton of a character as a graph from a character image](https://mlsanity.com/paper/1506.05068/) (2015)

## Abstract (arXiv)

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.