Abstract

An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. In contrast to previous approaches like the “neural gas” method of Martinetz and Schulten (1991, 1994), this model has no parameters which change over time and is able to continue learning, adding units and connections, until a performance criterion has been met. Applications of the model include vector quantization, clustering, and interpolation.

PDF: http://papers.nips.cc/paper/893-a-growing-neural-gas-network-learns-topologies.pdf

Interactive demo (2017): https://www.demogng.de/