The Representer Theorem for Hilbert Spaces: A Necessary and Sufficient Condition

Abstract

The representer theorem is a property that lies at the foundation of regularization theory and kernel methods. A class of regularization functionals is said to admit a linear representer theorem if every member of the class admits minimizers that lie in the finite dimensional subspace spanned by the representers of the data. A recent characterization states that certain classes of regularization functionals with differentiable regularization term admit a linear representer theorem for any choice of the data if and only if the regularization term is a radial nondecreasing function. In this paper, we extend such result by weakening the assumptions on the regularization term. In particular, the main result of this paper implies that, for a sufficiently large family of regularization functionals, radial nondecreasing functions are the only lower semicontinuous regularization terms that guarantee existence of a representer theorem for any choice of the data.

Cite

Text

Dinuzzo and Schölkopf. "The Representer Theorem for Hilbert Spaces: A Necessary and Sufficient Condition." Neural Information Processing Systems, 2012.

Markdown

[Dinuzzo and Schölkopf. "The Representer Theorem for Hilbert Spaces: A Necessary and Sufficient Condition." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/dinuzzo2012neurips-representer/)

BibTeX

@inproceedings{dinuzzo2012neurips-representer,
  title     = {{The Representer Theorem for Hilbert Spaces: A Necessary and Sufficient Condition}},
  author    = {Dinuzzo, Francesco and Schölkopf, Bernhard},
  booktitle = {Neural Information Processing Systems},
  year      = {2012},
  pages     = {189-196},
  url       = {https://mlanthology.org/neurips/2012/dinuzzo2012neurips-representer/}
}