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.

Cite

Text

Negrinho and Martins. "Orbit Regularization." Neural Information Processing Systems, 2014.

Markdown

[Negrinho and Martins. "Orbit Regularization." Neural Information Processing Systems, 2014.](https://mlanthology.org/neurips/2014/negrinho2014neurips-orbit/)

BibTeX

@inproceedings{negrinho2014neurips-orbit,
  title     = {{Orbit Regularization}},
  author    = {Negrinho, Renato and Martins, Andre},
  booktitle = {Neural Information Processing Systems},
  year      = {2014},
  pages     = {3221-3229},
  url       = {https://mlanthology.org/neurips/2014/negrinho2014neurips-orbit/}
}