Semi-supervised Variational Multi-view Anomaly Detection

Multi-view anomaly detection (Multi-view AD) is a challenging problem due to the inconsistent behaviors across multiple views. Meanwhile, learning useful representations with little or no supervision has attracted much attention in machine learning. There are a large amount of recent advances in representation learning focusing on deep generative models, such as Variational Auto Encoder (VAE). In this study, by utilizing the representation learning ability of VAE and manipulating the latent variables properly, we propose a novel Bayesian generative model as a semi-supervised multi-view anomaly detector, called MultiVAE. We conduct experiments to evaluate the performance of MultiVAE on multi-view data. The experimental results demonstrate that MultiVAE outperforms the state-of-the-art competitors across popular datasets for semi-supervised multi-view AD. As far as we know, this is the first work that applies VAE-based deep models on multi-view AD.