  In this paper, we introduce a neighbor embedding framework for manifold
alignment. We demonstrate the efficacy of the framework using a
manifold-aligned version of the uniform manifold approximation and projection
algorithm. We show that our algorithm can learn an aligned manifold that is
visually competitive to embedding of the whole dataset.
