Non-Convex Feature Learning via L_p, Inf Operator

AAAI 2014 pp. 1918-1924

doi:10.1609/AAAI.V28I1.9010 /aaai/2014/kong2014aaai-non/

Abstract

We present a feature selection method for solving sparse regularization problem, which hasa composite regularization of $\ell_p$ norm and $\ell_{\infty}$ norm.We use proximal gradient method to solve this \L1inf operator problem, where a simple but efficient algorithm is designed to minimize a relatively simple objective function, which contains a vector of $\ell_2$ norm and $\ell_\infty$ norm. Proposed method brings some insight for solving sparsity-favoring norm, andextensive experiments are conducted to characterize the effect of varying $p$ and to compare with other approaches on real world multi-class and multi-label datasets.

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Cite

Text

Kong and Ding. "Non-Convex Feature Learning via L_p, Inf Operator." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.9010

Markdown

[Kong and Ding. "Non-Convex Feature Learning via L_p, Inf Operator." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/kong2014aaai-non/) doi:10.1609/AAAI.V28I1.9010

BibTeX

@inproceedings{kong2014aaai-non,
  title     = {{Non-Convex Feature Learning via L_p, Inf Operator}},
  author    = {Kong, Deguang and Ding, Chris H. Q.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2014},
  pages     = {1918-1924},
  doi       = {10.1609/AAAI.V28I1.9010},
  url       = {https://mlanthology.org/aaai/2014/kong2014aaai-non/}
}