Learning Rates of l^q Coefficient Regularization Learning with Gaussian Kernel

Regularization is a well-recognized powerful strategy to improve the performance of a learning machine and l^q regularization schemes with are central in use. It is known that different q leads to different properties of the deduced estimators, say, l² regularization leads to a smooth estimator, while l¹ regularization leads to a sparse estimator. Then how the generalization capability of l^q regularization learning varies with q is worthy of investigation. In this letter, we study this problem in the framework of statistical learning theory. Our main results show that implementing l^q coefficient regularization schemes in the sample-dependent hypothesis space associated with a gaussian kernel can attain the same almost optimal learning rates for all ⁠. That is, the upper and lower bounds of learning rates for l^q regularization learning are asymptotically identical for all ⁠. Our finding tentatively reveals that in some modeling contexts, the choice of q might not have a strong impact on the generalization capability. From this perspective, q can be arbitrarily specified, or specified merely by other nongeneralization criteria like smoothness, computational complexity or sparsity.