  Anomaly detection in data analysis is an interesting but still challenging
research topic in real world applications. As the complexity of data dimension
increases, it requires to understand the semantic contexts in its description
for effective anomaly characterization. However, existing anomaly detection
methods show limited performances with high dimensional data such as ImageNet.
Existing studies have evaluated their performance on low dimensional, clean and
well separated data set such as MNIST and CIFAR-10. In this paper, we study
anomaly detection with high dimensional and complex normal data. Our
observation is that, in general, anomaly data is defined by semantically
explainable features which are able to be used in defining semantic
sub-clusters of normal data as well. We hypothesize that if there exists
reasonably good feature space semantically separating sub-clusters of given
normal data, unseen anomaly also can be well distinguished in the space from
the normal data. We propose to perform semantic clustering on given normal data
and train a classifier to learn the discriminative feature space where anomaly
detection is finally performed. Based on our careful and extensive experimental
evaluations with MNIST, CIFAR-10, and ImageNet with various combinations of
normal and anomaly data, we show that our anomaly detection scheme outperforms
state of the art methods especially with high dimensional real world images.
