We propose a framework for feature extraction from learned low-dimensional subspaces that
represent inter-subject variability. The manifold subspace is built from data-driven regions of
interest (ROI). The regions are learned via sparse regression using the mini-mental state
examination (MMSE) score as an independent variable which correlates better with the
actual disease stage than a discrete class label. The sparse regression is used to perform
variable selection along with a re-sampling scheme to reduce sampling bias. We then use …