Feature Elimination in Kernel Machines in moderately high dimensions
This work addresses feature selection for kernel-based models, which is incremental as it builds on existing methods with theoretical guarantees.
The paper tackles feature elimination in kernel machines by developing a recursive elimination method, showing it is uniformly consistent under certain assumptions and demonstrating its performance through simulations.
We develop an approach for feature elimination in statistical learning with kernel machines, based on recursive elimination of features.We present theoretical properties of this method and show that it is uniformly consistent in finding the correct feature space under certain generalized assumptions.We present four case studies to show that the assumptions are met in most practical situations and present simulation results to demonstrate performance of the proposed approach.