One-class Support Vector Machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and widely adopted in both research as well as industrial applications. The biggest issue for OC-SVM is, however, the capability to operate with large and high-dimensional datasets due to inefficient features and optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or auto-encoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class SVM (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descend to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.

提出了基于自编码器的一类支持向量机（AE-1SVM）模型，结合表示学习架构，采用随机傅里叶特征来逼近径向基核，集成了终端到终端的训练，可用于异常检测，并且拓展了深度学习在异常检测中的可能应用，并且，该模型在无监督的异常检测任务中表现显著优于之前采用分离训练的工作。

可扩展解释性深度学习和随机傅里叶特征的单类支持向量机