Meaning of the position of winning nodes and distance map about minisom

[JustGlowing]

hi @EuCot

The fact that A and B are mapped in areas with different response of the distance map might suggest that they're far in the input space but it's not always the case. The winning positions in the map tell you more on how close they are they are adjacent in your example.

[EuCot]

Thank you. So you are suggesting to rely mainly on the position on the map of the input data rather than on the relation between their position and the "weight" of each cell of the distance map?

But again, I know SOM is somehow a stochastic process (unless you use PCA initialization, in that case you always get the same result because it's deterministic), but what can the distance map tell us if it changes so deeply at each run of the model?

[JustGlowing]

Thank you. So you are suggesting to rely mainly on the position on the map of the input data rather than on the relation between their position and the "weight" of each cell of the distance map?

Usually the positions on the map and the weights are related (hence the distance map). However, it's not always the case.

But again, I know SOM is somehow a stochastic process (unless you use PCA initialization, in that case you always get the same result because it's deterministic), but what can the distance map tell us if it changes so deeply at each run of the model?

Just that the model is sensitive to the initial conditions. If you think that something is wrong though, check if the quantization and topological error are going down through the training.