Leveraging the Christoffel-Darboux Kernel for Online Outlier Detection - LAAS - Laboratoire d'Analyse et d'Architecture des Systèmes Access content directly
Preprints, Working Papers, ... Year : 2022

Leveraging the Christoffel-Darboux Kernel for Online Outlier Detection


Outlier detection is a subject of interest in data mining. With the evolution of data acquisition methods such as wireless sensor networks, there is a need to detect outliers in data streams. However, dealing with data streams is challenging due to the amount of data that grows infinitely and the nonstationarity of the distribution. On top of that, the detection has generally to be done in an unsupervised way. Some methods have been proposed to tackle this problem but none of them can be easily parameterized. This paper proposes a novel method based on the Christoffel-Darboux kernel borrowed from the theory of approximation and orthogonal polynomials. This method perfectly applies to data streams while being deployable with no tuning at all. It is compared to some state-of-the-art methods for outlier detection in data streams and applied to the data from an industrial luggage conveyor showing very convincing results.
Fichier principal
Vignette du fichier
icde_submission.pdf (3.71 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03562614 , version 1 (09-02-2022)


  • HAL Id : hal-03562614 , version 1


Kévin Ducharlet, Louise Travé-Massuyès, Jean-Bernard Lasserre, Marie-Véronique Le Lann, Youssef Miloudi. Leveraging the Christoffel-Darboux Kernel for Online Outlier Detection. 2022. ⟨hal-03562614⟩
58 View
117 Download


Gmail Facebook X LinkedIn More