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Outlier detection of clustered functional data with image and signal processing applications by archetype analysis

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  • Aleix Alcacer
  • Irene Epifanio

Abstract

In this study, we introduce an innovative methodology for anomaly detection of curves, applicable to both multivariate and multi-argument functions. This approach distinguishes itself from prior methods by its capability to identify outliers within clustered functional data sets. We achieve this by extending the recent AA + kNN technique, originally designed for multivariate analysis, to functional data contexts. Our method demonstrates superior performance through a comprehensive comparative analysis against twelve state-of-the-art techniques, encompassing simulated scenarios with either a single functional cluster or multiple clusters. Additionally, we substantiate the effectiveness of our approach through its application in three distinct computer vision tasks and a signal processing problem. To facilitate transparency and replication of our results, we provide access to both the code and the datasets used in this research.

Suggested Citation

  • Aleix Alcacer & Irene Epifanio, 2024. "Outlier detection of clustered functional data with image and signal processing applications by archetype analysis," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-23, November.
    • Handle: RePEc:plo:pone00:0311418
    DOI: 10.1371/journal.pone.0311418
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    References listed on IDEAS

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