  Learning to improve AUC performance is an important topic in machine
learning. However, AUC maximization algorithms may decrease generalization
performance due to the noisy data. Self-paced learning is an effective method
for handling noisy data. However, existing self-paced learning methods are
limited to pointwise learning, while AUC maximization is a pairwise learning
problem. To solve this challenging problem, we innovatively propose a balanced
self-paced AUC maximization algorithm (BSPAUC). Specifically, we first provide
a statistical objective for self-paced AUC. Based on this, we propose our
self-paced AUC maximization formulation, where a novel balanced self-paced
regularization term is embedded to ensure that the selected positive and
negative samples have proper proportions. Specially, the sub-problem with
respect to all weight variables may be non-convex in our formulation, while the
one is normally convex in existing self-paced problems. To address this, we
propose a doubly cyclic block coordinate descent method. More importantly, we
prove that the sub-problem with respect to all weight variables converges to a
stationary point on the basis of closed-form solutions, and our BSPAUC
converges to a stationary point of our fixed optimization objective under a
mild assumption. Considering both the deep learning and kernel-based
implementations, experimental results on several large-scale datasets
demonstrate that our BSPAUC has a better generalization performance than
existing state-of-the-art AUC maximization methods.
