mlsanityData to 7 Oct 2026

Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection

arXiv 1909.03495 ↗First posted 8 Sep 2019stat.ML cs.LGcited by 19

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Abstract (arXiv)

We present a method to compute the Shapley values of reconstruction errors of principal component analysis (PCA), which is particularly useful in explaining the results of anomaly detection based on PCA. Because features are usually correlated when PCA-based anomaly detection is applied, care must be taken in computing a value function for the Shapley values. We utilize the probabilistic view of PCA, particularly its conditional distribution, to exactly compute a value function for the Shapely values. We also present numerical examples, which imply that the Shapley values are advantageous for explaining detected anomalies than raw reconstruction errors of each feature.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Sep 2019.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 4; 2024: 2; 2025: 2; 2026: 4.

19 papers.

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  1. 2022A Survey on Explainable Anomaly Detectioncited by 39
  2. 2021Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)cited by 15
  3. 2023Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Modelcited by 10
  4. 2021Explainable Machine Learning for Fraud Detectioncited by 9
  5. 2021Ensembles of Random SHAPscited by 8
  6. 2023Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videoscited by 7
  7. 2021An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Datacited by 6
  8. 2025Control-flow anomaly detection by process mining-based feature extraction and dimensionality reductioncited by 6
  9. 2022Explaining Anomalies using Denoising Autoencoders for Financial Tabular Datacited by 4
  10. 2022RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanationscited by 2
  11. 2023Interpretable Ensembles of Hyper-Rectangles as Base Modelscited by 2
  12. 2024AcME-AD: Accelerated Model Explanations for Anomaly Detectioncited by 2
7 more
  1. 2024Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-ADcited by 2
  2. 2023Try with Simpler -- An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detectioncited by 1
  3. 2025On Using the Shapley Value for Anomaly Localization: A Statistical Investigationcited by 1
  4. 2026Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detectioncited by 1
  5. 2026RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithmscited by 0
  6. 2026Statistical Analysis of using the Shapley Value for Sensor Anomaly Localization with Accurate Classifierscited by 0
  7. 2026Witnesses Explain Anomaliescited by 0