Is it possible to use kernel principal component analysis (kPCA) for Latent Semantic Indexing (LSI) in the same way as PCA is used?
I perform LSI in R using the
prcomp PCA function and extract the features with highest loadings from the first $k$ components. By that I get the features describing the component best.
I tried to use the
kpca function (from the
kernlib package) but cannot see how to access the weights of the features to a principal component. Is this possible overall when using kernel methods?