A General Framework for Sparsity Regularized Feature Selection via Iteratively Reweighted Least Square Minimization

AAAI 2017 pp. 2471-2477

doi:10.1609/AAAI.V31I1.10833 /aaai/2017/peng2017aaai-general/

Abstract

A variety of feature selection methods based on sparsity regularization have been developed with different loss functions and sparse regularization functions. Capitalizing on the existing sparsity regularized feature selection methods, we propose a general sparsity feature selection (GSR-FS) algorithm that optimizes a ℓ2,r (0 < r ≤ 2) based loss function with a ℓ2,p-norm (0 < p ≤ 2) sparse regularization. The ℓ2,r-norm (0 <

PDF AAAI Semantic Scholar

Cite

Text

Peng and Fan. "A General Framework for Sparsity Regularized Feature Selection via Iteratively Reweighted Least Square Minimization." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.10833

Markdown

[Peng and Fan. "A General Framework for Sparsity Regularized Feature Selection via Iteratively Reweighted Least Square Minimization." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/peng2017aaai-general/) doi:10.1609/AAAI.V31I1.10833

BibTeX

@inproceedings{peng2017aaai-general,
  title     = {{A General Framework for Sparsity Regularized Feature Selection via Iteratively Reweighted Least Square Minimization}},
  author    = {Peng, Hanyang and Fan, Yong},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {2471-2477},
  doi       = {10.1609/AAAI.V31I1.10833},
  url       = {https://mlanthology.org/aaai/2017/peng2017aaai-general/}
}