I rather like isolation kernel methods: https://arxiv.org/pdf/2109.14198.pdf

The idea is that you take a random subset of points, and then work out for each point in your dataset which one of those random points it is closest to.

Repeat that process some large number of times. Points that regularly get mapped to the same exemplar are obviously close to each other.

For some tasks that's enough. Otherwise, if you feed that out into something else (e.g. t-SNE) and get much better results than if you try to reduce dimensionality directly.