Modified sparse functional principal component analysis for fMRI data process

Sparse and functional principal component analysis is a technique to extract sparse and smooth principal components from a matrix. In this paper, we propose a modified sparse and functional principal component analysis model for feature extraction. We measure the tuning parameters by their robustness against random perturbation, and select the tuning parameters by derivative-free optimization. We test our algorithm on the ADNI dataset to distinguish between the patients with Alzheimer's disease and the control group. By applying proper classification methods for sparse features, we get better result than classic singular value decomposition, support vector machine and logistic regression.