ML Anthology

10, 000+ Times Accelerated Robust Subset Selection

Feiyun Zhu, Bin Fan, Xinliang Zhu, Ying Wang, Shiming Xiang, Chunhong Pan
AAAI 2015 pp. 3217-3224
doi:10.1609/AAAI.V29I1.9565 /aaai/2015/zhu2015aaai-times/

Abstract

Subset selection from massive data with noised information is increasingly popular for various applications. This problem is still highly challenging as current methods are generally slow in speed and sensitive to outliers. To address the above two issues, we propose an accelerated robust subset selection (ARSS) method. Extensive experiments on ten benchmark datasets verify that our method not only outperforms state of the art methods, but also runs 10,000+ times faster than the most related method.

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Cite

Text

Zhu et al. "10, 000+ Times Accelerated Robust Subset Selection." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9565

Markdown

[Zhu et al. "10, 000+ Times Accelerated Robust Subset Selection." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/zhu2015aaai-times/) doi:10.1609/AAAI.V29I1.9565

BibTeX

@inproceedings{zhu2015aaai-times,
  title     = {{10, 000+ Times Accelerated Robust Subset Selection}},
  author    = {Zhu, Feiyun and Fan, Bin and Zhu, Xinliang and Wang, Ying and Xiang, Shiming and Pan, Chunhong},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {3217-3224},
  doi       = {10.1609/AAAI.V29I1.9565},
  url       = {https://mlanthology.org/aaai/2015/zhu2015aaai-times/}
}