Minimax sparse principal subspace estimation in high dimensions
نویسندگان
چکیده
منابع مشابه
Minimax Sparse Principal Subspace Estimation in High Dimensions
We study sparse principal components analysis in high dimensions , where p (the number of variables) can be much larger than n (the number of observations), and analyze the problem of estimating the subspace spanned by the principal eigenvectors of the population covariance matrix. We prove optimal, non-asymptotic lower and upper bounds on the minimax subspace estimation error under two differe...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2013
ISSN: 0090-5364
DOI: 10.1214/13-aos1151