The RaPKod package implements a kernel method made for outlier detection. Namely, given a data set of reference typical observation (non-outliers or inliers), it tests each new observation in an online way to determine whether it is an outlier or not. This method uses random low-dimensional projections in a kernel space to build a test statistic whose asymptotic null-distribution (ie when the tested observation is not an outlier) is known. The RaPKod method has two parameters: gamma - the hyperparameter of the (Gaussian) kernel used - and p - the dimensionality of the random projection in the kernel space.
The package consists of two functions: the main function "rapkod" and the auxilary function "od.opt.param" which computes optimal parameters values in RaPKod.
Maintainer: Jeremie Kellner <firstname.lastname@example.org>
Kellner J., "Gaussian Models and Kernel Methods", PhD thesis, Universite des Sciences et Technologies de Lille (defended on December 1st, 2016)
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