The robust nearest shrunken centroids classifier for high-dimensional heavy-tailed data
The robust nearest shrunken centroids classifier for high-dimensional heavy-tailed data
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DOI：
10.1214/22-ejs2022
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发表时间：
2022-01
影响因子：
1.1
通讯作者：
Shaokang Ren;Qing Mai
中科院分区：
文献类型：
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作者：
Shaokang Ren;Qing Mai
最近收缩质心分类器（NSC）是一种流行的高维分类器。然而，当数据具有重尾性时，很容易出现不准确的分类。在本文中，我们开发了一个强大的一般化的NSC（RNSC），仍然有效，在这种情况下。通过在评分函数的估计和计算中引入Huber损失，我们减少了重尾的影响。我们严格地显示变量的选择，估计和预测的一致性，在高维弱矩条件下。从经验上讲，我们的建议大大优于NSC和许多其他成功的分类器，当数据是重尾的，而在没有重尾的情况下，仍然与NSC相当。在一个真实的数据例子中也证明了RNSC的良好性能。
: The nearest shrunken centroids classifier (NSC) is a popular high-dimensional classifier. However, it is prone to inaccurate classification when the data is heavy-tailed. In this paper, we develop a robust general- ization of NSC (RNSC) which remains effective under such circumstances. By incorporating the Huber loss both in the estimation and the calcula- tion of the score function, we reduce the impacts of heavy tails. We rigorously show the variable selection, estimation, and prediction consistency in high dimensions under weak moment conditions. Empirically, our proposal greatly outperforms NSC and many other successful classifiers when data is heavy-tailed while remaining comparable to NSC in the absence of heavy tails. The favorable performance of RNSC is also demonstrated in a real data example.