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In this paper, a multi-layer architecture (in a hierarchical fashion) bystacking various Kernel Ridge Regression (KRR) based Auto-Encoder for one-classclassification is proposed and is referred as MKOC. MKOC has many layers ofAuto-Encoders to project the input features into new feature space and the lastlayer was regression based one class classifier. The Auto-Encoders use anunsupervised approach of learning and the final layer uses semi-supervised(trained by only positive samples) approach of learning. The proposed MKOC isexperimentally evaluated on 15 publicly available benchmark datasets.Experimental results verify the effectiveness of the proposed approach over 11existing state-of-the-art kernel-based one-class classifiers. Friedman test isalso performed to verify the statistical significance of the claim of thesuperiority of the proposed one-class classifiers over the existingstate-of-the-art methods.

Multi-layer Kernel Ridge Regression for One-class Classification