SelfOrganizing Map (SOM) is a type of artificial neural network that is trained using unsupervised learning to produce a lowdimensional (typically twodimensional), discretized representation of the input space of the training samples, called a map, and is, therefore, a method to do dimensionality reduction. Selforganizing maps differ from other artificial neural networks as they apply competitive learning as opposed to errorcorrection learning (such as backpropagation with gradient descent), and in the sense that they use a neighborhood function to preserve the topological properties of the input space. This makes SOMs useful for visualizing lowdimensional views of highdimensional data. Like most artificial neural networks, SOMs operate in two modes: training and mapping. “Training” builds the map using input, while “mapping” automatically classifies a new input vector. A selforganizing 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 twodimensional regular spacing in a hexagonal or rectangular grid. The procedure for placing a vector from data space onto the map is to find the node with the closest weight vector to the data space vector.
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