2014 NIPS NeurIPS 2014

Orbit Regularization

Abstract

We propose a general framework for regularization based on group 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 lie in the convex hull of this orbit (the orbitope). Common regularizers are recovered as particular cases, and a connection is revealed between the recent sorted 1 -norm and the hyperoctahedral group. 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.

🧭 Keyword Pioneer — group action
🐝 Cross-Pollinator — Computer Vision, Machine Learning, Mathematics & Optimization
🌉 Interdisciplinary Bridge — Machine Learning and Mathematics & Optimization