PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semi-supervised kernel regression using whitened function classes
Matthias Franz, Y. Kwon, Carl Edward Rasmussen and Bernhard Schölkopf
In: 26th DAGM Symposium, 30 Aug - 01 Sep 2004, Tuebingen, Germany.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:377
Deposited By:Matthias Franz
Deposited On:18 December 2004