On the asymptotic normality and efficiency of Kronecker envelope principal component analysis
This paper is a study on the asymptotic analysis. The authors show the multilinear principal component analysis (PCA) theory and the notion of the Kronecker envelope and proposed dimension folding estimators for supervised dimension reduction. The authors review the asymptotic normality of the principal component (PC) estimators of ordinary PCA, they derive the asymptotic normality of the PC estimators of Kronecker envelope PCA. The authors show that Kronecker envelope PCA is asymptotically more efficient than ordinary PCA. They then illustrate with real data example of cryo-EM image clustering and a simulation experiments to conclusion their results.
multilinear principal component analysis
scientific article; zbMATH DE number 7361744
scientific article