Refinable Kernels

Abstract

Motivated by mathematical learning from training data, we introduce the notion of refinable kernels. Various characterizations of refinable kernels are presented. The concept of refinable kernels leads to the introduction of wavelet-like reproducing kernels. We also investigate a refinable kernel that forms a Riesz basis. In particular, we characterize refinable translation invariant kernels, and refinable kernels defined by refinable functions. This study leads to multiresolution analysis of reproducing kernel Hilbert spaces.

Cite

Text

Xu and Zhang. "Refinable Kernels." Journal of Machine Learning Research, 2007.

Markdown

[Xu and Zhang. "Refinable Kernels." Journal of Machine Learning Research, 2007.](https://mlanthology.org/jmlr/2007/xu2007jmlr-refinable/)

BibTeX

@article{xu2007jmlr-refinable,
  title     = {{Refinable Kernels}},
  author    = {Xu, Yuesheng and Zhang, Haizhang},
  journal   = {Journal of Machine Learning Research},
  year      = {2007},
  pages     = {2083-2120},
  volume    = {8},
  url       = {https://mlanthology.org/jmlr/2007/xu2007jmlr-refinable/}
}