Does embedding projector use dimensional reduction?

In addition to the very good previous answers, I want to add a few things due to completeness reasons:

Often yes (probably 99% of all examples I saw in data science), but only if the dimensional space is lower than before in the original space. So this statement is not true without exception…

Let’s take a PCA. It’s closely related to the implementation of a singular value decomposition: one application here is a modal transformation in structural dynamics, which is done to decouple interactions in the system so that it is easier to analyse e.g. eigenfrequencies, eigenmodes etc. in a simpler way.

(Often of course the benefit of model order reduction is used in this context, too! Almost the same accuracy with a way better computational performance can be achieved if done well which is often the way to go). But I want to highlight that singular value decomposition (SVD) or PCA could also be theoretically performed in the full original space without loss of information because in the end only a linear transformation is done.

If you are interested in structural dynamics, feel free to take a look.

Hope that helps!

Best regards

1 Like