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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 l(2) loss, in the joint limit as dimension and sample size increase to inf...

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Detalhes bibliográficos
Main Authors: Birnbaum, Aharon, Johnstone, Iain M., Nadler, Boaz, Paul, Debashis
Formato: Artigo
Idioma:Inglês
Publicado em: 2013
Assuntos:
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC4196701/
https://ncbi.nlm.nih.gov/pubmed/25324581
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1214/12-AOS1014
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