Sub-clusters of Normal Data for Anomaly Detection

TL;DR

This paper introduces a novel anomaly detection approach that leverages semantic clustering of normal high-dimensional data to improve detection accuracy, especially on complex datasets like ImageNet.

Contribution

It proposes a semantic clustering and classification framework that enhances anomaly detection performance on high-dimensional, complex datasets, surpassing existing methods.

Findings

Outperforms state-of-the-art anomaly detection methods on ImageNet.

Effective in distinguishing anomalies in high-dimensional image data.

Works well with various normal and anomaly data combinations.

Abstract

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.