10,000+ Times Accelerated Robust Subset Selection (ARSS)

Zhu, Feiyun, Fan, Bin, Zhu, Xinliang, Wang, Ying, Xiang, Shiming, Pan, Chunhong

arXiv.org Machine Learning 

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. Specifically in the subset selection area, this is the first attempt to employ the $\ell_{p}(0

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