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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.
