  A brain can detect outlier just by using only normal samples. Similarly,
one-class classification (OCC) also uses only normal samples to train the model
and trained model can be used for outlier detection. In this paper, a
multi-layer architecture for OCC is proposed by stacking various Graph-Embedded
Kernel Ridge Regression (KRR) based Auto-Encoders in a hierarchical fashion.
These Auto-Encoders are formulated under two types of Graph-Embedding, namely,
local and global variance-based embedding. This Graph-Embedding explores the
relationship between samples and multi-layers of Auto-Encoder project the input
features into new feature space. The last layer of this proposed architecture
is Graph-Embedded regression-based one-class classifier. The Auto-Encoders use
an unsupervised approach of learning and the final layer uses semi-supervised
(trained by only positive samples and obtained closed-form solution) approach
to learning. The proposed method is experimentally evaluated on 21 publicly
available benchmark datasets. Experimental results verify the effectiveness of
the proposed one-class classifiers over 11 existing state-of-the-art
kernel-based one-class classifiers. Friedman test is also performed to verify
the statistical significance of the claim of the superiority of the proposed
one-class classifiers over the existing state-of-the-art methods. By using two
types of Graph-Embedding, 4 variants of Graph-Embedded multi-layer KRR-based
one-class classifier has been presented in this paper. All 4 variants performed
better than the existing one-class classifiers in terms of various discussed
criteria in this paper. Hence, it can be a viable alternative for OCC task. In
the future, various other types of Auto-Encoders can be explored within
proposed architecture.
