self organising 3d detectors in smartphone | Telecom Consumer Discussion Forums

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.

 

A self-organizing map showing U.S. Congressvoting patterns visualized in Synapse. The first two boxes show clustering and distances while the remaining ones show the component planes. Red means a yes vote while blue means a no vote in the component planes (except the party component where red is Republican and blue is Democratic).

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.^[1][2] The Kohonen net is a computationally convenient abstraction building on work on biologically neural models from the 1970s^[3] and morphogenesis models dating back to Alan Turing in the 1950s^[4]

Like most artificial neural networks, SOMs operate in two modes: training and mapping. “Training” builds the map using input examples (acompetitive process, also called vector quantization), while “mapping” automatically classifies a new input vector.