Feature elimination in kernel machines in moderately high dimensions
提出一种基于递归消除的核机器特征消除方法，证明其在广义假设下能一致地找到正确特征空间，并通过案例和模拟验证了方法的有效性。
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 a few case studies to show that the assumptions are met in most practical situations and present simulation results to demonstrate performance of the proposed approach.