According to this website, kernel PCA with RBF (Gaussian) kernel can separate half-moon shapes and concentric circles effectively but not Swiss Roll shapes (in 3-D).
I don't understand why it doesn't work with Swiss Roll and how the point in 3-D is actually mapped to a point in a higher dimension. The article stated that
a (Gaussian) radial basis function (RBF) kernel can be used to map the data onto infinite dimensions
but I don't understand it. What are these "infinite dimensions"?
Also, can you give me an intuitive guideline in which distribution of the cluster of data I should apply RBF and in which cases I should avoid using it?