10,000+ Times Accelerated Robust Subset Selection
DOI:
https://doi.org/10.1609/aaai.v29i1.9565Abstract
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
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Section
Main Track: Novel Machine Learning Algorithms