Sparse variable PCA using a steepest descent on a Grassman manifold

摘要

penalized PCA criterion and optimize it by steepest descent along geodesic on a Grassman manifold. This ensures that each step obeys PCA orthogonality as well as an invariance property of the criterion. We show in simulations that it outperforms a previous svPCA algorithm and apply it to a real high dimensional functional Magnetic Resonance Imaging (fMRI) data.