  Unsupervised anomaly detection aims to identify anomalous samples from highly
complex and unstructured data, which is pervasive in both fundamental research
and industrial applications. However, most existing methods neglect the complex
correlation among data samples, which is important for capturing normal
patterns from which the abnormal ones deviate. In this paper, we propose a
method of Correlation aware unsupervised Anomaly detection via Deep Gaussian
Mixture Model (CADGMM), which captures the complex correlation among data
points for high-quality low-dimensional representation learning. Specifically,
the relations among data samples are correlated firstly in forms of a graph
structure, in which, the node denotes the sample and the edge denotes the
correlation between two samples from the feature space. Then, a dual-encoder
that consists of a graph encoder and a feature encoder, is employed to encode
both the feature and correlation information of samples into the
low-dimensional latent space jointly, followed by a decoder for data
reconstruction. Finally, a separate estimation network as a Gaussian Mixture
Model is utilized to estimate the density of the learned latent vector, and the
anomalies can be detected by measuring the energy of the samples. Extensive
experiments on real-world datasets demonstrate the effectiveness of the
proposed method.
