1.4 Kohonen feature map

https://data-information-meaning.blogspot.com/2020/12/14-kohenen-feature-maps-new-embedded.html

Kohenen Feature Maps are an interesting blend of two processes, simultaneously creating a model and imposing on that model a new metric space, it works by taking a simple KMeans learning function and blend it with a graph.
...
This is an important point, may be I should expand on it now, but I will get back to it again. Just quickly mention, two aspects of creating the embedded space. 1) It echoes the original data, things that are similar in the original space are similar in the new embedded space, sounds good right, not really, it keeps the lower level connected to the higher level, there is less learning going on, since the higher levels are not free from the constraints of the lower level. 2) When the data distribution changes, the model is locked into the original metrics (true that Joshua Benjio tries to avoid this with 'attention' but why create a problem and then try to work around it)