论文标题
降低特征空间作为异常检测的数据预处理
Feature space reduction as data preprocessing for the anomaly detection
论文作者
论文摘要
在本文中，我们提出了两个管道，以减少使用一类SVM进行异常检测的特征空间。作为两个管道的第一阶段，我们比较了三个卷积自动编码器的性能。我们将PCA方法与T-SNE一起用作第一个管道，而基于重建错误的方法作为第二种。两种方法都具有异常检测的潜力，但是重建误差指标对此任务均更强。我们表明，卷积自动编码器体系结构对此任务没有重大影响，我们证明了在现实世界数据集中的方法。
In this paper, we present two pipelines in order to reduce the feature space for anomaly detection using the One Class SVM. As a first stage of both pipelines, we compare the performance of three convolutional autoencoders. We use the PCA method together with t-SNE as the first pipeline and the reconstruction errors based method as the second. Both methods have potential for the anomaly detection, but the reconstruction error metrics prove to be more robust for this task. We show that the convolutional autoencoder architecture doesn't have a significant effect for this task and we prove the potential of our approach on the real world dataset.
