Fast, Exact Bootstrap Principal Component Analysis for p > 1 million.

ABSTRACT: Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subject (p) is much larger than the number of subjects (n), calculating and storing the leading principal components from each bootstrap sample can be computationally infeasible. To address this, we outline methods for fast, exact calculation of bootstrap principal components, eigenvalues, and scores. Our methods leverage the fact that all bootstrap samples occupy the same n-dimensional subspace as the original sample. As a result, all bootstrap principal components are limited to the same n-dimensional subspace and can be efficiently represented by their low dimensional coordinates in that

SUBMITTER: Fisher A 

PROVIDER: S-EPMC5014451 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature