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Communication Dans Un Congrès Année : 2018

Outlier detection in high-dimensional spaces using one-dimensional neighborhoods

Résumé

Detecting outliers in a dataset is a problem with numerous applications in data analysis for fields such as medical care, finance, and banking or network surveillance. But in a majority of use-cases, data points are described by a lot of features, making outlier detection more complicated. As the number of dimensions increases, the notion of proximity becomes less meaningful: this is due to sparser data and elements becoming almost equally distant from each other. Medical datasets add a layer of complexity caused by their heterogeneous nature. Because of these caveats, standard algorithms become less relevant when hundred of dimensions are involved. This paper discusses the benefits of an outlier detection algorithm that uses a simple concept of one-dimensional neighborhood observations to circumvent the problems mentioned previously.
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Dates et versions

hal-02088256 , version 1 (15-04-2019)

Identifiants

  • HAL Id : hal-02088256 , version 1

Citer

Joris Falip, Frédéric Blanchard, Michel Herbin. Outlier detection in high-dimensional spaces using one-dimensional neighborhoods. Fouille de Données Complexes (FDC@EGC), 2018, Paris, France. ⟨hal-02088256⟩

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