Universalities of reproducing kernels revisited

Abstract

Kernel methods have been widely applied to machine learning and other questions of approximating an unknown function from its finite sample data. To ensure arbitrary accuracy of such an approximation, various denseness conditions are imposed on the selected kernel. This note contributes to the study of universal, characteristic, and -universal kernels. We first give a simple and direct description of the difference and relation among these three kinds of universalities of kernels. We then focus on translation-invariant and Hilbert–Schmidt kernels formed by polynomials. A simple and shorter proof of the known characterization of characteristic translation-invariant kernels will be presented. The main purpose of the note is to give a delicate discussion on the universalities of Hilbert–Schmidt kernels formed by weighted polynomials.

Keywords:

• kernel methods
• universal kernels
• characteristic kernels
• density
• translation-invariant kernels
• weighted polynomial kernels

AMS Subject Classifications:

• 41A30
• 68T05

Acknowledgements

The authors would like to express their appreciation to the anonymous reviewers for their useful comments, which greatly improve the presentation of the paper.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

Supported in part by Natural Science Foundation of China under [grant number 11222103] and [grant number 11101438], and by the US Army Research Office under [grant number W911NF-12-1-0163].