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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Franz, M.O., Kwon, Y., Rasmussen, C.E., Schölkopf, B. (2004). Semi-supervised Kernel Regression Using Whitened Function Classes. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_3
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DOI: https://doi.org/10.1007/978-3-540-28649-3_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22945-2
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