Sparse equisigned PCA: Algorithms and performance bounds in the noisy rank-1 setting
نویسندگان
چکیده
منابع مشابه
Minimax Bounds for Sparse Pca with Noisy High-dimensional Data.
We study the problem of estimating the leading eigenvectors of a high-dimensional 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 ris...
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We study the problem of estimating the leading eigenvectors of a high-dimensional 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 ris...
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ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2020
ISSN: 1935-7524
DOI: 10.1214/19-ejs1657