Optimal Margin Distribution Additive Machine

In recent years, sparse additive machines have attracted increasing attention in high dimensional classification due to their flexibility and representation interpretability. However, most of the existing methods are formulated under Tikhonov regularization schemes associated with the hinge loss, where the distribution information of observations is neglected usually. To circumvent this problem, we propose an optimal margin distribution additive machine (called ODAM) by incorporating the optimal margin distribution strategy into sparse additive models. The proposed approach can be implemented by a dual coordinate descent algorithm and its empirical effectiveness is confirmed on simulated and benchmark datasets.