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I was wondering if the features selected with RFE with SVM linear kernel are still "good" features when we use a non linear model, like SVM rbf kernel. This comes in practice when you want to use SVM as a classifier for the RFE but you are forced to stick to linear kernel: maybe you can do the selection with SVM linear and then the prediction with SVM rbf? If I would answer I would say yes: the features selected with linear SVM are able to explain a linear relationship between descriptors and output, so if we use a non linear model they are still useful. If you can give an explation, it will be quite appreciated.

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