SOM is a data visualization technology that reduces the dimensions of data through the use of self-organizing neural network, to help us to understand the high dimensional data.

Initialize the map with random weight vectors.

for t = 0 ~ 1

select a sample randomly from the set of training data

every node is examined and find the best match unit --- (a)

choose neighbors and scale neighbors --- (b)

increase t

end

(a): go through all the weight vector and calculate the distance of each weight to the sample.

(b): different methods to choose neighbors, such like Gaussian, or within a radius R. The new value is: current value * (1 –t) + sample vector * t.

Disadvantages: Need a value for each dimension of each sample. It is very computationally expensive.

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