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Sep, 2014
10,000+次加速的稳健子集选择(ARSS)
10,000+ Times Accelerated Robust Subset Selection (ARSS)
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Feiyun Zhu, Bin Fan
TL;DR
提出加速鲁棒子集选择方法(ARSS),以防止大误差对目标的影响,并通过ALM和等价推导降低计算成本,从而在实验中证明方法的有效性。
Abstract
subset selection
from massive data with degraded information is increasingly popular for various applications. This problem is still highly challenging due to the low speed and the sensitivity to
outliers
of exis
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