Statistical and computational trade-offs in estimation of sparse principal components

Introduction

Principal Component Analysis (PCA), which involves projecting a sample of multivariate data onto the space spanned by the leading eigenvectors of the sample covariance matrix, is one of the oldest and most widely-used dimension reduction devices in statistics. It has proved to be particularly effective when the dimension of the data is relatively small by comparison with the sample size. However, the work of