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. © Springer-Verlag 2004.
CITATION STYLE
Franz, M. O., Kwon, Y., Rasmussen, C. E., & Schölkopf, B. (2004). Semi-supervised kernel regression using whitened function classes. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3175, 18–26. https://doi.org/10.1007/978-3-540-28649-3_3
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