Self-organizing maps
Robust growing neural gas algorithm with application in cluster analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
A concept learning network based on correlation and backpropagation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Growing Neural Gas is a self organizing network capable to build a lattice of neural unit that grows in the input pattern manifold. The structure of the obtained network often is not a planar graph and can be not suitable for visualization; cluster identification is possible only if a set of not connected subgraphs are produced. In this work we propose a method to select the neural units in order to extract the information on the pattern clusters, even if the obtained network graph is connected. The proposed method creates a new structure called Labeling Network (LNet) that repeats the topology of the GNG network and a set of weights to the links of the neuron graph. These weights are trained using an anti-Hebbian algorithm obtaining a new structure capable to label input patterns according to their cluster.