No, UMAP is nonlinear. The general idea is that you generate a neighborhood graph of your data points, do a spectral embedding on that to get your initial result, and then do gradient descent to make its neighborhood graph closer to the high-dimensional one.
I guess it's a Principle Component Analysis (PCA) dimensionality reduction so the axes are not necessarily concepts/features with names. More just "abstract dimensions of similarity."
The underlying UMAP model is actually pretty interesting. It's linked to in the tour, though I would have expected it to be featured more prominently: https://pair-code.github.io/understanding-umap/
The color dimension is configurable, so it makes sense.
But I don't get what the 3 dimensions of spatial coordinates are, and how to change them.
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Alright my bad it's actually explained in the "tour": https://pair-code.github.io/understanding-umap/
It seems to be some kind of multivariate PCA.