Abnormal Subspace Sparse PCA for Anomaly Detection and Interpretation

TL;DR

This paper introduces ASPCA, an interpretable sparse PCA model for anomaly detection that maintains detection accuracy while providing meaningful interpretations of anomalies.

Contribution

The paper proposes a novel sparse PCA-based model that enhances interpretability in anomaly detection without sacrificing accuracy.

Findings

ASPCA achieves detection accuracy comparable to traditional PCA.

ASPCA provides meaningful interpretations for detected anomalies.

Experiments on synthetic and real datasets validate the model's effectiveness.

Abstract

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.