Sparse PCA for High-Dimensional Data With Outliers
نویسندگان
چکیده
منابع مشابه
Sparse PCA for High-Dimensional Data With Outliers
A new sparse PCA algorithm is presented which is robust against outliers. The approach is based on the ROBPCA algorithm which 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 find...
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We study the problem of estimating the leading eigenvectors of a highdimensional population covariance matrix based on independent Gaussian observations. We establish a lower bound on the minimax risk of estimators under the l2 loss, in the joint limit as dimension and sample size increase to infinity, under various models of sparsity for the population eigenvectors. The lower bound on the risk...
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ژورنال
عنوان ژورنال: Technometrics
سال: 2016
ISSN: 0040-1706,1537-2723
DOI: 10.1080/00401706.2015.1093962