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My thoughts with PCA would be that we could eliminate a large number of the dimensions stored in the tree by only taking the X main components, and make a transform which combines a large number of measurements from all sorts of things which aren't even very useful and turn them into a much more information-dense, lower dimension location. This would save on memory as well as search time while still keeping pretty much exactly the same results.

I agree that far too much effort has been put into refining weights, but it does have its place for ekking out that extra little bit of performance against a known population.