  Kernel method-based one-class classifier is mainly used for outlier or
novelty detection. In this letter, kernel ridge regression (KRR) based
one-class classifier (KOC) has been extended for learning using privileged
information (LUPI). LUPI-based KOC method is referred to as KOC+. This
privileged information is available as a feature with the dataset but only for
training (not for testing). KOC+ utilizes the privileged information
differently compared to normal feature information by using a so-called
correction function. Privileged information helps KOC+ in achieving better
generalization performance which is exhibited in this letter by testing the
classifiers with and without privileged information. Existing and proposed
classifiers are evaluated on the datasets from UCI machine learning repository
and also on MNIST dataset. Moreover, experimental results evince the advantage
of KOC+ over KOC and support vector machine (SVM) based one-class classifiers.
