We propose a general framework for regularization based on group-induced majorization. In this framework, a group is defined to act on the parameter space and an orbit is fixed; to control complexity, the model parameters are confined to the convex hull of this orbit (the orbitope). We recover several well-known regularizers as particular cases, and reveal a connection between the hyperoctahedral group and the recently proposed sorted l1-norm. We derive the properties a group must satisfy for being amenable to optimization with conditional and projected gradient algorithms. Finally, we suggest a continuation strategy for orbit exploration, presenting simulation results for the symmetric and hyperoctahedral groups.