Manifold-aligned Neighbor Embedding
This work addresses manifold alignment for data visualization, but appears incremental as it adapts an existing method (UMAP) without clear broader impact.
The paper tackles the problem of manifold alignment by introducing a neighbor embedding framework, demonstrating its efficacy with a manifold-aligned version of UMAP that learns an aligned manifold visually competitive to embeddings of the whole dataset.
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.