Abnormal Subspace Sparse PCA for Anomaly Detection and Interpretation

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 PCA-based model for anomaly detection and interpretation. The propose ASPCA model constructs principal components with sparse and o...

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Veröffentlicht in:arXiv.org 2016-05
Hauptverfasser: Xingyan Bin, Zhao, Ying, Shen, Bilong
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Sprache:eng
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Anomalies
Model accuracy
Principal components analysis
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Zhao, Ying
Shen, Bilong
description 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.
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subjects Anomalies
Model accuracy
Principal components analysis
title Abnormal Subspace Sparse PCA for Anomaly Detection and Interpretation
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