Algorithm 1.

ACC Training

Initialization:

Randomly assign positive class sample i to cluster l(i), for i ∈ {1, …, N+} and l(i) ∈ {1, …, L}.

repeat

Classification Step:

Train an SLSVM classifier for each cluster of positive samples combined with all negative samples. Each classifier is the outcome of a quadratic optimization problem (cf. (11)) and provides a hyperplane perpendicular to βl and a corresponding optimal objective value Ol.

Re-clustering Step:

Re-cluster the positive samples based on the classifiers βl and update the l(i)’s.

until no l(i) is changed or ΣlOl is not decreasing.