Selecting the Number of Principal Components with SURE

Principal component analysis (PCA) is one of the most widely used methods in multivariate signal processing. An important problem is to select the number of principal components (PCs). In this paper we develop an automatic method for selecting the number of PCs based on Stein's unbiased risk estimator (SURE). In simulations the new method outperforms state of the art cross-validation methods.