We propose a semi-supervised approach towards anomaly detection in multivariate categorical data. Our goal is to learn a model that can distinguish the anomalous data, given a small set of training data from the normal class. To this end, our approach learns the probability distribution of normal instances with the assumption that the categorical data are generated from a continuous latent space. Gaussian process is adopted to construct the generative model. As a non-parametric Bayesian model, Gaussian process can adapt its model complexity according to the data size. Hence, our approach can be effective when the training dataset is small. Comprehensive experiments over different benchmarks clearly demonstrate the effectiveness of our approach.

我们提出了一种在多元分类数据中进行异常检测的半监督方法。我们的目标是学习一个模型，该模型可以从正常班级的少量训练数据中区分出异常数据。为此，我们的方法假设分类数据是从连续的潜在空间生成的，从而学习了正常实例的概率分布。采用高斯过程构造生成模型。作为非参数贝叶斯模型，高斯过程可以根据数据大小调整其模型复杂度。因此，当训练数据集较小时，我们的方法可能有效。在不同基准上进行的全面实验清楚地证明了我们方法的有效性。