Open Access
February 2015 Sparsistency and agnostic inference in sparse PCA
Jing Lei, Vincent Q. Vu
Ann. Statist. 43(1): 299-322 (February 2015). DOI: 10.1214/14-AOS1273


The presence of a sparse “truth” has been a constant assumption in the theoretical analysis of sparse PCA and is often implicit in its methodological development. This naturally raises questions about the properties of sparse PCA methods and how they depend on the assumption of sparsity. Under what conditions can the relevant variables be selected consistently if the truth is assumed to be sparse? What can be said about the results of sparse PCA without assuming a sparse and unique truth? We answer these questions by investigating the properties of the recently proposed Fantope projection and selection (FPS) method in the high-dimensional setting. Our results provide general sufficient conditions for sparsistency of the FPS estimator. These conditions are weak and can hold in situations where other estimators are known to fail. On the other hand, without assuming sparsity or identifiability, we show that FPS provides a sparse, linear dimension-reducing transformation that is close to the best possible in terms of maximizing the predictive covariance.


Download Citation

Jing Lei. Vincent Q. Vu. "Sparsistency and agnostic inference in sparse PCA." Ann. Statist. 43 (1) 299 - 322, February 2015.


Published: February 2015
First available in Project Euclid: 6 February 2015

zbMATH: 1308.62125
MathSciNet: MR3311861
Digital Object Identifier: 10.1214/14-AOS1273

Primary: 62H12

Keywords: agnostic inference , principal components analysis , Sparsity , subspace estimation , Variable selection

Rights: Copyright © 2015 Institute of Mathematical Statistics

Vol.43 • No. 1 • February 2015
Back to Top