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

Aleix Alcacer and Irene Epifanio

PLOS ONE, 2024, vol. 19, issue 11, 1-23

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.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0311418

DOI: 10.1371/journal.pone.0311418

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