Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization Benjamin Aubin

We consider a commonly studied supervised classification of a synthetic dataset whose labels are generated by feeding a one-layer neural network with random i.i.d inputs. We study the generalization performances of standard classifiers in the high-dimensional regime where α = n/d is kept finite in the limit of a high dimension d and number of samples n.