The Annals of Statistics
- Ann. Statist.
- Volume 41, Number 6 (2013), 3074-3110.
Sparse PCA: Optimal rates and adaptive estimation
Principal component analysis (PCA) is one of the most commonly used statistical procedures with a wide range of applications. This paper considers both minimax and adaptive estimation of the principal subspace in the high dimensional setting. Under mild technical conditions, we first establish the optimal rates of convergence for estimating the principal subspace which are sharp with respect to all the parameters, thus providing a complete characterization of the difficulty of the estimation problem in term of the convergence rate. The lower bound is obtained by calculating the local metric entropy and an application of Fano’s lemma. The rate optimal estimator is constructed using aggregation, which, however, might not be computationally feasible.
We then introduce an adaptive procedure for estimating the principal subspace which is fully data driven and can be computed efficiently. It is shown that the estimator attains the optimal rates of convergence simultaneously over a large collection of the parameter spaces. A key idea in our construction is a reduction scheme which reduces the sparse PCA problem to a high-dimensional multivariate regression problem. This method is potentially also useful for other related problems.
Ann. Statist. Volume 41, Number 6 (2013), 3074-3110.
First available in Project Euclid: 1 January 2014
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62H12: Estimation
Secondary: 62H25: Factor analysis and principal components; correspondence analysis 62C20: Minimax procedures
Cai, T. Tony; Ma, Zongming; Wu, Yihong. Sparse PCA: Optimal rates and adaptive estimation. Ann. Statist. 41 (2013), no. 6, 3074--3110. doi:10.1214/13-AOS1178. https://projecteuclid.org/euclid.aos/1388545679
- Supplementary material: Supplement to “Sparse PCA: Optimal rates and adaptive estimation”. We provide proofs for all the remaining theoretical results in the paper. The proofs rely on results in [17, 19, 20, 25, 31, 33] and .