Self Organizing Map (SOM)

What is Self Organizing Map (SOM) A neural network is called a mapping network if it is able to compute some functional relationship between its input and its output. For example if the input to a network is the value of an angle and the output is the cosine of that angle, the network perform the mapping θ =cos (θ). For such a simple function, we do not need a Neural Network. However we might want to perform a complicated mapping where does not know how to describe the functional relationship in advance, but we do know of examples of the correct mapping. In this situation, Neural Network is applicable to discover its own algorithms which is extremely useful. Self Organizing Map: Overview The SOM algorithm is based on unsupervised, competitive learning. It provides a topology preserving mapping from the high dimensional space to map units. Map units, or neurons, usually form a two-dimensional lattice and thus the mapping is a mapping from high dimensional space onto a plane. The property of topology preserving means that the mapping preserves the relative distance between the points. Points that are near each other in the input space are mapped to nearby map…

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