KOC+: Kernel ridge regression based one-class classification using privileged information

Abstract

A kernel-based one-class classifier is mainly used for outlier or novelty detection. Kernel ridge regression (KRR) based methods have received quite a lot of attention in recent years due to its non-iterative approach of learning. In this paper, KRR-based one-class classifier (KOC) has been extended for learning using privileged information (LUPI) framework. LUPI-based KOC method is referred to as KOC+ in this paper. This privileged information is available as feature/features of the dataset, but only during training (not during testing). KOC+ utilizes privileged features information differently compared to other features information. It uses this information in KOC+ by the help of so-called correction function. This information helps KOC+ in achieving better generalization performance. Existing and proposed classifiers are evaluated on the datasets taken from UCI machine learning repository and MNIST dataset. Moreover, experimental results exhibit that KOC+ outperforms KOC and other LUPI-based state-of-the-art one-class classifiers. Source code of this paper is provided on the corresponding author’s GitHub homepage:https://github.com/Chandan-IITI/KOCPlus_or_OCKELMPlus_or_OCLSSVMPlus