Is it possible to have a PCA setup (or any other dimensionality reduction technique) in a way that adding new features wouldn't require retrain downstream models that were trained on that particular PCA?

The idea is that (hopefully) we could have new features that would improve PCA in a way that wouldn't require retraining every time we add new features. If this isn't possible, then is this possible to achive in another way?

We add new features regularly by adding new datasources, or add some new features through feature engineering. Downflow models rely on PCA of that feature space. By training models on PCA and not on features directly simplifies workflows, and also removes burden of retraining models if it was possible to have a PCA (or another feature extraction process that maps original features to this "stable" subspace).

Is it possible to have a PCA setup (or any other dimensionality reduction technique) in a way that adding new features wouldn't require retrain downstream models that were trained on that particular PCA?

The idea is that (hopefully) we could have new features that would improve PCA in a way that wouldn't require retraining every time we add new features. If this isn't possible as explained here, how to achieve this in another way?

We add new features regularly by adding new datasources, or add some new features through feature engineering. Downflow models rely on PCA of that feature space. By training models on PCA and not on features directly simplifies workflows, and also removes burden of retraining models if it was possible to have a PCA (or another feature extraction process) that maps original features to this "stable" subspace.