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neural-networkunsupervised-learningsomself-organizing-maps

Self organized map understaning

I have a self organized map created a Som_pak-3.1 here

here

If I have three different type of elements, and they are different. Why the elements are not in different parts of the map? Why the "A", "B" and "C" are in many many cases together at the same hexagon? Why "B" and "C" are never alone in an hexagon?

Thanks in advance!

Solution

  • I feel that it is a normal result for SOM. The unsupervised SOM algorithm is not aware of the elements. Using the distance metric, the neurons have learned the vectors, and then the elements were placed as labels at the best matching neuron.

    One possible reason for two elements appearing on the same node is if they have the same values for each of the features. Otherwise, they have different values for each feature, but the values still seem similar according to the distance metric.

    The spatial resolution can be increased by increasing the map size. This may allow the classes to be separable. However, the trade-off is that statistical significance of each neuron goes down when it is associated with fewer data points. So what I would suggest is that you can try different sizes of maps to find the one that is appropriate for your data set and goals.

    Actually I was just reading about this exact point, p. 19 in Kohonen's book "MATLAB Implementations and Applications of the Self-Organizing Map" available at http://docs.unigrafia.fi/publications/kohonen_teuvo/. It covers the MATLAB SOM-Toolkit that was created after SOM-PAK. The book only briefly covers SOM-PAK but I believe that the theory from the book would help out.