Humans can detect outliers just by using only observations of normal samples. Similarly, one-class classification (OCC) uses only normal samples to train a classification model which can be used for outlier detection. This paper proposes a multi-layer architecture for OCC by stacking various graph-embedded kernel ridge regression (KRR)-based autoencoders in a hierarchical fashion. We formulate the autoencoders under the graph-embedding framework to exploit local and global variance criteria. The use of multiple autoencoder layers allows us to project the input features into a new feature space on which we apply a graph-embedded regression-based one-class classifier. We build the proposed hierarchical OCC architecture in a progressive manner and optimize the parameters of each of the successive layers based on closed-form solutions. The performance of the proposed method is evaluated on 21 balanced and 20 imbalanced datasets. The effectiveness of the proposed method is indicated by the experimental results over 11 existing state-of-the-art kernel-based one-class classifiers. Friedman test is also performed to verify the statistical significance of the obtained results. By using two types of graph-embedding, 4 variants of graph-embedded multi-layer KRR-based one-class classification methods are presented in this paper. All 4 variants have performed better than the existing one-class classifiers in terms of the various performance metrics. Hence, they can be a viable alternative for OCC for a wide range of one-class classification tasks. As a future extension, various other autoencoder variants can be applied within the proposed architecture to increase efficiency and performance.

人类仅通过观察正常样本即可检测到异常值。同样，一类分类（OCC）仅使用正常样本来训练可用于离群值检测的分类模型。本文通过以分层方式堆叠各种基于图嵌入的内核岭回归（KRR）的自动编码器，为OCC提出了一种多层体系结构。我们在图嵌入框架下制定自动编码器，以利用局部和全局方差准则。多个自动编码器层的使用使我们可以将输入要素投影到新要素空间中，在该要素空间上应用基于图嵌入的基于回归的一类分类器。我们以渐进方式构建提出的分层OCC架构，并基于封闭形式的解决方案优化每个连续层的参数。该方法的性能在21个平衡和20个不平衡数据集上进行了评估。在11个现有的基于内核的最先进的一类分类器上的实验结果表明了该方法的有效性。还执行Friedman检验以验证所获得结果的统计显着性。通过使用两种类型的图嵌入，提出了基于图嵌入的多层基于KRR的一类分类方法的4种变体。就各种性能指标而言，所有4个变体的性能均优于现有的一类分类器。因此，对于一系列一类分类任务，它们可以作为OCC的可行替代方案。作为未来的扩展，