Extract reduced dimensions from PCA


I'm trying to include some of the dimension reduction technique, which already exist into a workflow framework. As far as I understood PCA, the PCA-compute & -apply nodes create vectors for all number-variables. After that I well replace all my number-variables by those vectors and can continue using other model learners and predictors.

But this means: I will have continue to working on a "unreadable" data set. Would it be possible, to somehow reduce the original data set (dimensions) on basis of the vectors calculated by PCA? (e.g. like the Correlation Filter?)


Thanks you in advance


as far as I know this is not possible. But there are many other ways to reduce dimensionality, for example, forward feature selection or backward feature elimination. 

Under the following link you can find a blog post where seven techniques for dimensionality reduction are explained