Data Centering in Feature Space

Marina Meilă

Data Centering in Feature Space

Marina Meilă

Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, PMLR R4:209-216, 2003.

Abstract

This paper presents a family of methods for data translation in feature space, to be used in conjunction with kernel machines. The translations are performed using only kernel evaluations in input space. We use the methods to improve the numerical properties of kernel machines. Experiments with synthetic and real data demonstrate the effectiveness of data centering and highlight other interesting aspects of translation in feature space.

Cite this Paper

BibTeX


@InProceedings{pmlr-vR4-meila03a,
  title = 	 {Data Centering in Feature Space},
  author =       {Meil\u{a}, Marina},
  booktitle = 	 {Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics},
  pages = 	 {209--216},
  year = 	 {2003},
  editor = 	 {Bishop, Christopher M. and Frey, Brendan J.},
  volume = 	 {R4},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {03--06 Jan},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/r4/meila03a/meila03a.pdf},
  url = 	 {https://proceedings.mlr.press/r4/meila03a.html},
  abstract = 	 {This paper presents a family of methods for data translation in feature space, to be used in conjunction with kernel machines. The translations are performed using only kernel evaluations in input space. We use the methods to improve the numerical properties of kernel machines. Experiments with synthetic and real data demonstrate the effectiveness of data centering and highlight other interesting aspects of translation in feature space.},
  note =         {Reissued by PMLR on 01 April 2021.}
}

Endnote

%0 Conference Paper
%T Data Centering in Feature Space
%A Marina Meilă
%B Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics
%C Proceedings of Machine Learning Research
%D 2003
%E Christopher M. Bishop
%E Brendan J. Frey	
%F pmlr-vR4-meila03a
%I PMLR
%P 209--216
%U https://proceedings.mlr.press/r4/meila03a.html
%V R4
%X This paper presents a family of methods for data translation in feature space, to be used in conjunction with kernel machines. The translations are performed using only kernel evaluations in input space. We use the methods to improve the numerical properties of kernel machines. Experiments with synthetic and real data demonstrate the effectiveness of data centering and highlight other interesting aspects of translation in feature space.
%Z Reissued by PMLR on 01 April 2021.

APA


Meilă, M.. (2003). Data Centering in Feature Space. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R4:209-216 Available from https://proceedings.mlr.press/r4/meila03a.html. Reissued by PMLR on 01 April 2021.

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