  Multi-view data refers to a setting where features are divided into feature
sets, for example because they correspond to different sources. Stacked
penalized logistic regression (StaPLR) is a recently introduced method that can
be used for classification and automatically selecting the views that are most
important for prediction. We introduce an extension of this method to a setting
where the data has a hierarchical multi-view structure. We also introduce a new
view importance measure for StaPLR, which allows us to compare the importance
of views at any level of the hierarchy. We apply our extended StaPLR algorithm
to Alzheimer's disease classification where different MRI measures have been
calculated from three scan types: structural MRI, diffusion-weighted MRI, and
resting-state fMRI. StaPLR can identify which scan types and which derived MRI
measures are most important for classification, and it outperforms elastic net
regression in classification performance.
