Not competent on Ivis but UMAP is very good at making sure that points that are close in the original space are close in the output space. When you want to per

Not competent on Ivis but UMAP is very good at making sure that points that are close in the original space are close in the output space. When you want to perform some sort of clustering on top of the dimensionality reduction its a very useful property. Note that is it very common to get rid of 90% of the dimensionality with a PCA and then to apply something like UMAP on top to improve speed (and because PCA can be trusted to keep the relevant information for later postprocessig which cannot be said of other techniques).