Viewing a single comment thread. View all comments

imyourzer0 t1_iy35ydo wrote

I don’t know why people worry so much about the state of the art. Sometimes, the right tool just happens to have already existed for a while. In a lot of cases, PCA works just fine, or to the point where something much more current won’t give you a much better answer. Like another commenter has already said, depending on the assumptions you can or are willing to make, the best choice needn’t be at the bleeding edge.

38

olmec-akeru OP t1_iy362iv wrote

Cool, I get this—but I think its important not to keep ones head in the sand. There are new techniques and its important to grok them.

23

imyourzer0 t1_iy5unj3 wrote

I certainly wouldn’t advise anyone to ignore new methods. That’s a point well taken. I’m only saying that when you have a working method, and its assumptions about your data can be validated (either logically or with tests), you don’t need to start looking for SOTA methods.

Really, the goal is solving a problem, and to do that, you want to find the most appropriate method—not just the newest method. It’s one thing to “keep up with the Joneses” so that when problems arise you know as many of the available tools as possible, but picking an algorithm usually doesn’t depend on whether that algorithm is new.

3

ktpr t1_iy3v2i1 wrote

Unfortunately, deeply understanding your problem and its relationship to prior algorithms is a lot more work than just telling someone that you applied a SOTA algorithm and got decent results.

edit - an apostrophe

15

olmec-akeru OP t1_iy7aszg wrote

Completely correct.

The corollary remains true though: applying the correct algorithm is a function of knowing the set of available algorithms. The newness of the algorithm isn't a ranking feature.

2