I get the question but at the same time I don't. It really depends on what your goal is.

Case in point: I can use little more than PCA and maybe a bit of diffusion maps. There are fundamental properties that make me need these Can other methods separate better? Sure! There are some really neat pictures you can make. But, to get those pictures, there are changes that need to be made for my data that make things a bit unworkable unless I can invert them, and generally, I can't. This doesn't matter to other people, but it's everything to me.

State of the art is what you make of it. For me? PCA still is, as it will be for many others. Doesn't matter if you can separate better, that's like the least of my interests. That being said, what is it for you?

Note: I hate quantitative classification benchmarks in general so any take I have related to "SOTA" will always be biased.