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Outlier detection in multivariate functional data based on a geometric aggregation

Lejeune, Clément and Mothe, Josiane and Teste, Olivier Outlier detection in multivariate functional data based on a geometric aggregation. (2020) In: International Conference on Extending Database Technology (EDBT 2020), 30 March 2020 - 2 April 2020 (Copenhagen, Denmark).

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Official URL: https://doi.org/10.5441/002/edbt.2020.38

Abstract

The increasing ubiquity of multivariate functional data (MFD) requires methods that can properly detect outliers within such data, where a sample corresponds to $p>1$ parameters observed with respect to (w.r.t) a continuous variable (extit{e.g.} time). We improve the outlier detection in MFD by adopting a geometric view on the data space while combining the new data representation with state-of-the-art outlier detection algorithms. The geometric representation of MFD as paths in the $p$-dimensional Euclidean space enables to implicitly take into account the correlation w.r.t the continuous variable between the parameters. We experimentally show that our method is robust to various rates of outliers in the training set when fitting the outlier detection model and can detect outliers which are not detected by standard algorithms.

Item Type:Conference or Workshop Item (Paper)
HAL Id:hal-02942772
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Other partners > Airbus (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
Funders:
ANRT : Association Nationale de la Recherche et de la Technologie (France)
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Deposited On:08 Sep 2020 12:56

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