Self-Organizing Maps, sometimes called Kohonen networks, are a special class of neural
networks. A self-organizing map consists of neurons placed at the nodes of a two-dimensional lattice. The neurons become selectively activated to various input mass spectra, or classes of spectra, in the course of a competitive learning process. The neurons compete among themselves to be activated or excluded. You can view SOM as a nonlinear generalization of PCA.
The principal goal of self-organizing maps is to transform a set of n
-dimensional input spectra into a discrete two-dimensional map and to display this transformation. Each input spectrum presented to the network activates a neuron according to a complex set of interrelationships between spectra. In SOM, each mass spectrum must always activate a neuron and this spectrum is shown on the particular neuron. Spectra that activate the same neuron belong, in terms of classification, to the same pattern. To ensure that the self-organizing process has a chance to develop properly, the networks should be exposed to a certain number of different spectra. As a result, this application requires a minimum of 10 spectra in a self-organizing process.