How to choose PCA or KernelPCA a priori?
Lots of the dimensionality problems are trial and errors at first.

Only very few datasets are linear "manifolds" that can be described with PCA. But it's a good start to see if there are disjoint sets, or if you can figure out some structure in 2D or 3D.

KPCA is a good technique, but there are many different kernels you can try. You may want to start with other "better" techniques (better as more deterministic) like ISOMAP, Laplacian Eigenmaps or LLE.

One of the things to remember is that you need enough data to populate the embedded space.