Boosting with structural sparsity: A differential inclusion approach

@article{Huang2017BoostingWS,
title={Boosting with structural sparsity: A differential inclusion approach},
author={Chendi Huang and Xinwei Sun and Jiechao Xiong and Y. Yao},
journal={Applied and Computational Harmonic Analysis},
year={2017},
volume={48},
pages={1-45}
}
• Chendi Huang, +1 author Y. Yao
• Published 2017
• Mathematics
• Applied and Computational Harmonic Analysis
• Abstract Boosting as gradient descent algorithms is one popular method in machine learning. In this paper a novel Boosting-type algorithm is proposed based on restricted gradient descent with structural sparsity control whose underlying dynamics are governed by differential inclusions. In particular, we present an iterative regularization path with structural sparsity where the parameter is sparse under some linear transforms, based on variable splitting and the Linearized Bregman Iteration… CONTINUE READING

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