Regularized Principal Component Analysis

Abstract

Traditional principal component analysis (PCA) suffers from high estimation variability and low interpretability in high‐dimensional data analysis. This article presents several regularization approaches for PCA by imposing structural constraints on eigenvectors to avoid overfitting and ease interpretation. Applying shrinkage, thresholding, smoothing, or rotation on eigenvectors leads to regularized PCA with enhanced interpretability.