From black box to glass box\: Making UMAP interpretable with exact feature contributions

We transform UMAP from a black box into a glass box. By learning the embedding function with a certain type of deep network, we can compute equivalent linear mappings of the input features that exactly reconstruct each embedding, revealing the heretofore hidden logic of UMAP.