Couldn't you just do that to the test data and not do anything with your training data ?
That would be very bad , because uh your t your model was trained uh expecting uh , that wouldn't work. Because your model was trained expecting a certain var variance on C - one.
Uh - huh.
And because the model thinks C - one is important. After you train the model , you sort of  y you could do  you could do still what I was proposing initially , that during the training you  you compress C - one that becomes  then it becomes less important in a training.