Good Day to everyone. I have spent quite some time now, introducing myself to neural networks. Therefore i am also looking into SOM's. Of course also on this site, as far as i have potentially "duplicate questions". The Statement: "...finding a corresponding point between a representative of the input space and a representative from a lower dimensional space." But what does this actually mean? I can see the concept to adjust weights to move in the high dim. space. How does the movement in an m - dimensional space affect the representation in an n-m dimensional space? In other words: how can the initialization of a vector with n (n <=3 seems intuitive) dimensions be interpreted(mapped, assigned,...) meaningfully in i.e. 2D? (without ideas like: cut away everything but the first two data-rows). I can see the concept of using RGB colors, but is there a more general approach?
Thank you for your time and answers (or links that might contain an answer)