My question is three-fold
In the context of "Kernelized" support vector machines
- Is variable/feature selection desirable - especially since we regularize the parameter C to prevent overfitting and the main motive behind introducing kernels to a SVM is to increase the dimensionality of the problem, in such a case reducing the dimensions by parameter reduction seems counter-intuitive
- If the answer to the 1st question is "NO", then, On what conditions would the answer change that one should keep in mind ?
- Are there any good methods that have been tried to bring about feature reduction for SVMs in scikit-learn library of python - I have tried the SelectFpr method and am looking for people with experiences with different methods.