Sparse loading noisy PCA using an l0 penalty

In this paper we present a novel model based sparse principal component analysis method based on the l₀ penalty. We develop an estimation method based on the generalized EM algorithm and iterative hard thresholding and an associated model selection method based on Bayesian information criterion (BIC). The method is compared to a previous sparse PCA method using both simulated data and DNA microarray data.