Asymptotic properties of principal component analysis and shrinkage-bias adjustment under the generalized spiked population model.

ABSTRACT: With the development of high-throughput technologies, principal component analysis (PCA) in the high-dimensional regime is of great interest. Most of the existing theoretical and methodological results for high-dimensional PCA are based on the spiked population model in which all the population eigenvalues are equal except for a few large ones. Due to the presence of local correlation among features, however, this assumption may not be satisfied in many real-world datasets. To address this issue, we investigate the asymptotic behavior of PCA under the generalized spiked population model. Based on our theoretical results, we propose a series of methods for the consistent estimation of population eigenvalues, angles between the sample and population eigenvectors, correlation coefficients bet

SUBMITTER: Dey R 

PROVIDER: S-EPMC7441582 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature