From the README:
> PaCMAP (Pairwise Controlled Manifold Approximation) is a dimensionality reduction method that can be used for visualization, preserving both local and global structure of the data in original space. PaCMAP optimizes the low dimensional embedding using three kinds of pairs of points: neighbor pairs (pair_neighbors), mid-near pair (pair_MN), and further pairs (pair_FP).
> Previous dimensionality reduction techniques focus on either local structure (e.g. t-SNE, LargeVis and UMAP) or global structure (e.g. TriMAP), but not both, although with carefully tuning the parameter in their algorithms that controls the balance between global and local structure, which mainly adjusts the number of considered neighbors. Instead of considering more neighbors to attract for preserving glocal structure, PaCMAP dynamically uses a special group of pairs -- mid-near pairs, to first capture global structure and then refine local structure, which both preserve global and local structure. For a thorough background and discussion on this work, please read our paper.
I think doing so would be especially important in a paper on DR techniques which are already so fraught in how they are deployed (often with little thought) in many applied contexts, and when so much of their putative utility comes from their interaction with human visual perception. I would have loved to see some discussion of actual engineering use cases where PaCMAP proves more useful than t-SNE - I’m sure there are many! Really just nitpicking from me though, will probably try it out on my own cases in the next few days.
Fwiw, I use pacmap when building pipelines to get a feel whether a model is capturing signals as expected, for which it works better than the two due to the structure preserving making the conceptual mapping easier