  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 PCA-based model for anomaly detection and
interpretation. The propose ASPCA model 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 PCA model, and can provide
interpretations for individual anomalies.
