10,000+ Times Accelerated Robust Subset Selection

Authors

  • Feiyun Zhu Institute of Automation, Chinese Academy of Sciences
  • Bin Fan Institute of Automation, Chinese Academy of Sciences
  • Xinliang Zhu Institute of Automation, Chinese Academy of Sciences
  • Ying Wang Institute of Automation, Chinese Academy of Sciences
  • Shiming Xiang Institute of Automation, Chinese Academy of Sciences
  • Chunhong Pan Institute of Automation, Chinese Academy of Sciences

DOI:

https://doi.org/10.1609/aaai.v29i1.9565

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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Published

2015-02-21

How to Cite

Zhu, F., Fan, B., Zhu, X., Wang, Y., Xiang, S., & Pan, C. (2015). 10,000+ Times Accelerated Robust Subset Selection. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9565

Issue

Section

Main Track: Novel Machine Learning Algorithms