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Semi-supervised kernel regression using whitened function classes

2004

Conference Paper

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The use of non-orthonormal basis functions in ridge regression leads to an often undesired non-isotropic prior in function space. In this study, we investigate an alternative regularization technique that results in an implicit whitening of the basis functions by penalizing directions in function space with a large prior variance. The regularization term is computed from unlabelled input data that characterizes the input distribution. Tests on two datasets using polynomial basis functions showed an improved average performance compared to standard ridge regression.

Author(s): Franz, MO. and Kwon, Y. and Rasmussen, CE. and Schölkopf, B.
Book Title: Pattern Recognition, Proceedings of the 26th DAGM Symposium, Lecture Notes in Computer Science, Vol. 3175
Journal: Pattern Recognition, Proceedings of the 26th DAGM Symposium
Volume: LNCS 3175
Pages: 18-26
Year: 2004
Day: 0
Editors: CE Rasmussen and HH B{\"u}lthoff and MA Giese and B Sch{\"o}lkopf
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 26th DAGM Symposium
Event Place: Tübingen, Germany

Address: Berlin, Gerrmany
Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{2638,
  title = {Semi-supervised kernel regression using whitened function classes},
  author = {Franz, MO. and Kwon, Y. and Rasmussen, CE. and Sch{\"o}lkopf, B.},
  journal = {Pattern Recognition, Proceedings of the 26th DAGM Symposium},
  booktitle = {Pattern Recognition, Proceedings of the 26th DAGM Symposium, Lecture Notes in Computer Science, Vol. 3175},
  volume = {LNCS 3175},
  pages = {18-26},
  editors = {CE Rasmussen and HH B{\"u}lthoff and MA Giese and B Sch{\"o}lkopf},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Gerrmany},
  year = {2004}
}