Getting the contents of a fluid.labelset~ into a fluid.dataset~

[rodrigo.constanzo]

All the clusters above were done on the “raw” dimensions (21d) as I would imagine using it such a way where that is what’s in the fluid.kdtree~ as well. In other words, in this case the UMAP is just for visualization.

I haven’t played with this much at all really, but I anticipate using kmeans and such independently of any reduction, or rather avoiding reduction in general for real-time use since it’d be “slower” (the fitting of umap/etc…, but also all the pruning/peeking/composing around the process).

@tremblap

doing the clustering on the reduced dimensions (post UMAP) is interesting because you can distort the reduced space to get cluster shapes and content that you like. This could be done post-autoencoder too, or post-pca or post-mds. Just fun all round.

This sounds really interesting, but I fear by this you just mean just scaling min/max-type things (or std), as opposed to more elastically “distorting” the space, which is of definite interest.