 a self-organizing map (som) or self-organizing feature map (sofm) is a type of artificial neural network (ann) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map. self-organizing maps are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space.  this makes soms useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. the artificial neural network introduced by the finnish professor teuvo kohonen in the 1980s is sometimes called a kohonen map or network. the kohonen net is a computationally convenient abstraction building on work on biologically neural models from the 1970svon der malsburg, c. (1973). self-organization of orientation sensitive cells in the striate cortex. kybernetik, 14:85-100. and morphogenesis models dating back to alan turing in the 1950sturing, alan (1952). the chemical basis of morphogenesis, phil. trans. of the royal society, v. 237, pp. 5-72; london. like most artificial neural networks, soms operate in two modes: training and mapping. "training" builds the map using input examples (a competitive process, also called vector quantization), while "mapping" automatically classifies a new input vector. a self-organizing map consists of components called nodes or neurons. associated with each node are a weight vector of the same dimension as the input data vectors, and a position in the map space. the usual arrangement of nodes is a two-dimensional regular spacing in a hexagonal or rectangular grid. the self-organizing map describes a mapping from a higher-dimensional input space to a lower-dimensional map space. the procedure for placing a vector from data space o
Artificial_neural_networks Data_clustering_algorithms Dimension_reduction Finnish_inventions