Unsupervised Learning with Growing Neural Gas (GNG) Neural Network

Learns data clusters and their topology in n-dimensional space by using the Growing Neural Gas net.

The Growing Neural Gas (GNG) Neural Network belongs to the class of Topology Representing Networks (TRN's). It can learn supervised and unsupervised. Here, the on-line, unsupervised learning mode is implemented and demonstrated. It's learning method employs a combination of modified Kohonen learning to adjust the neuron's positions, with a Competitive Hebbian Learning (CHL) for its connections. For details please consult ref. [1]. In order to make the main script (gng_lax.m) functional, you must first select and generate a manifold (data) using the corresponding data generator. For a nice report on the family of competitive learning methods please consult ref. [2].
REFERENCE
[1] Fritzke B. "A Growing Neural Gas Network Learns Topologies", Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995.

[2] Fritzke B. "Some Competitive Learning Methods", 1997 available at: https://pdfs.semanticscholar.org/7f13/a0c932e32eb0dbe009dc86badfe8bed31e66.pdf