As the dimension increases, a decision-boundary hyperplane that has 1% test error rapidly gets extremely close to the equator of the sphere
What does the center of the sphere represent in this case?
(I’m imaging the training and test sets consisting of points in a highly dimensional space, and the classifier as drawing a hyperplane to mostly separate them from each other. But I’m not sure what point in this space would correspond to the “center”, or what sphere we’d be talking about.)
What does the center of the sphere represent in this case?
(I’m imaging the training and test sets consisting of points in a highly dimensional space, and the classifier as drawing a hyperplane to mostly separate them from each other. But I’m not sure what point in this space would correspond to the “center”, or what sphere we’d be talking about.)