2014
NeurIPS
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.
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Interdisciplinary Bridge
- Artificial Intelligence and Mathematics & Optimization
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Trend Setter
- Regularization
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Keyword Pioneer
- parameter space
Authors
Topics
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Optimization & Theory > Optimization
Mathematics & Optimization > Optimization > Optimization
Mathematics & Optimization > Optimization > Convex Optimization
Machine Learning > Learning Types > Regularization
Deep Learning > Optimization & Theory > Regularization