Abstract
This paper introduces Kernel Principal Component Regression (PCR) with the
Covariance Inflation Criterion (CIC) for model order selection. The relation to
Kernel Ridge Regression (RR) and other 'kernel' regression techniques is
given and two benchmark problems demonstrate the comparable performance of CIC to
cross-validation techniques. In all reported experiments CIC provides
the models with equal performance in comparison to Kernel RR. Moreover, on a significant real world application,
Kernel PCR with CIC resulted in smaller model compared
to Kernel PCR with the cross-validation technique employed for the selection of principal components.
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