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.