Sparse PCA for High-Dimensional Data With Outliers

被引:47
作者
Hubert, Mia [1 ]
Reynkens, Tom [1 ]
Schmitt, Eric [1 ]
Verdonck, Tim [1 ]
机构
[1] Katholieke Univ Leuven, Dept Math, Leuven, Belgium
关键词
Dimension reduction; Outlier detection; Robustness; PROJECTION-PURSUIT APPROACH; PRINCIPAL COMPONENTS; ROBUST PCA;
D O I
10.1080/00401706.2015.1093962
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
A new sparse PCA algorithm is presented, which is robust against outliers. The approach is based on the ROBPCA algorithm that generates robust but nonsparse loadings. The construction of the new ROSPCA method is detailed, as well as a selection criterion for the sparsity parameter. An extensive simulation study and a real data example are performed, showing that it is capable of accurately finding the sparse structure of datasets, even when challenging outliers are present. In comparison with a projection pursuit-based algorithm, ROSPCA demonstrates superior robustness properties and comparable sparsity estimation capability, as well as significantly faster computation time.
引用
收藏
页码:424 / 434
页数:11
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