Spectral ranking and unsupervised feature selection for point, collective, and contextual anomaly detection

Unsupervised anomaly detection algorithm is typically suitable only to a specific type of anomaly, among point anomaly, collective anomaly, and contextual anomaly. A mismatch between the intended anomaly type of an algorithm and the actual type in the data can lead to poor performance. In this paper, utilizing Hilbert-Schmidt independence criterion (HSIC), we propose an unsupervised backward elimination feature selection algorithm BAHSIC-AD to identify a subset of features with the strongest interdependence for anomaly detection. Using BAHSIC-AD, we compare the effectiveness of a recent Spectral Ranking for Anomalies (SRA) algorithm with other popular anomaly detection methods on a few synthetic datasets and real-world datasets. Furthermore, we demonstrate that SRA, combined with BAHSIC-AD, can be a generally applicable method for detecting point, collective, and contextual anomalies.