SVM-Based Multiple Instance Classification via DC Optimization

A multiple instance learning problem consists of categorizing objects, each represented
as a set (bag) of points. Unlike the supervised classification paradigm, where each point of the
training set is labeled, the labels are only associated with bags, while the labels of the points inside the
bags are unknown. We focus on the binary classification case, where the objective is to discriminate
between positive and negative bags using a separating surface. Adopting a support vector machine
setting at the training level, the problem of minimizing the classification-error function can be
formulated as a nonconvex nonsmooth unconstrained program. We propose a difference-of-convex
(DC) decomposition of the nonconvex function, which we face using an appropriate nonsmooth DC
algorithm. Some of the numerical results on benchmark data sets are reported.