Relation to "many vcs buffers"

I think this method has some relation with the popular many VCS buffers way, in which each VCS buffer forms a probability space, and by accumulating, the best group of probability space is chosen, and when having less data, it downgrades automatically.

k-nn with many dimensions does similar thing, only smoother imo.