Semisupervised spherical separation

We embed the concept of spherical separation of two disjoint finite sets of points into the semisupervised framework. This approach improves efficiency in the solution of real-world classification problems in which the number of unlabeled points is very large and labeling data is in general expensive. We develop a model characterized by an error function which is nonconvex and nondifferentiable, that we minimize by means of a bundle method. Numerical results on some small/large datasets drawn from literature are reported.