[loehnsberg]

Unfortunately, as you move into higher dimensions, these algorithms typically bog down.

Suppose that you have a point that is inside of the convex hull of the mesh that you want to use for triangulation (we‘re talking hyper-triangles here). What are the best points to choose for your triangulation? Since there are a lot of candidates for hyper-triangles you cannot possibly store the set of triangles beforehand.

I approached this problem using linear programming using the distance to the mesh points to find the best triangle. Not sure if this is the best approach though.

Happy to hear if someone knows of a better approach.

[tetris11]

Could you not do dimensional reduction first, run the algorithm on the first X components, map the result back onto the loadings?

[loehnsberg]

Most triangulation algorithms are designed for the two-dimensional plane, so this only works if the first two components are sufficient.

[tetris11]

Do a UMAP or tSNE, triangulate, map loadings back to features?