  Algorithmic feature learners provide high-dimensional vector representations
for non-matrix structured signals, like images, audio, text, and graphs.
Low-dimensional projections derived from these representations can be used to
explore variation across collections of these data. However, it is not clear
how to assess the uncertainty associated with these projections. We adapt
methods developed for bootstrapping principal components analysis to the
setting where features are learned from non-matrix data. We empirically compare
the derived confidence regions in simulations, varying factors that influence
both feature learning and the bootstrap. Approaches are illustrated on spatial
proteomic data. Code, data, and trained models are released as an R compendium.
