Regularization is a well-recognized powerful strategy to improve the performance of a learning machine and lq regularization schemes with [Formula: see text] 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 lq 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 lq coefficient regularization schemes in the sample-dependent hypothesis space associated with a gaussian kernel can attain the same almost optimal learning rates for all [Formula: see text]. That is, the upper and lower bounds of learning rates for lq regularization learning are asymptotically identical for all [Formula: see text]. 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.
Learning Rates of lq Coefficient Regularization Learning with Gaussian Kernel
