Abnormal Subspace Sparse PCA for Anomaly Detection and Interpretation

The main shortage of principle component analysis (PCA) based anomaly detection models is their interpretability. In this paper, our goal is to propose an interpretable PCAbased model for anomaly detection and interpretation. The propose ASPCAmodel constructs principal components with sparse and orthogonal loading vectors to represent the abnormal subspace, and uses them to interpret detected anomalies. Our experiments on a synthetic dataset and two real world datasets showed that the proposed ASPCA models achieved comparable detection accuracies as the PCAmodel, and can provide interpretations for individual anomalies.