I feel like this boils down to: imagine a frame from which you draw data sets for fitting a hyperplane to separate the data into two classes. The direction of t

I feel like this boils down to: imagine a frame from which you draw data sets for fitting a hyperplane to separate the data into two classes. The direction of the hyperplane has lower variance if you impose regularization on the norm. In a high dimensional, linear space you can add epsilon to lots and lots of dimensions in such a way that you can cross that hyperplane, and not all the regularization of the norm in the world will save you. Both statements are fairly obvious if you've studied functional analysis and probability. This is a case where pre-ML computer vision didn't have the same problem. I've always felt that something got lost in the transition.