Monday, September 26, 2011

Self-Organizing Map (SOM)

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

(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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