Abstract
An outlier detection method for high dimensional data is presented in this paper. It makes use of a robust and regularized estimation of the covariance matrix which is achieved by maximization of a penalized version of the likelihood function for joint location and inverse scatter. A penalty parameter controls the amount of regularization.
The algorithm is computation intensive but provides higher efficiency than other methods. This fact will be demonstrated in an example with simulated data, in which the presented method is compared to another algorithm for high dimensional data.
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Gschwandtner, M., Filzmoser, P. (2013). Outlier Detection in High Dimension Using Regularization. In: Kruse, R., Berthold, M., Moewes, C., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Advances in Intelligent Systems and Computing, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_26
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DOI: https://doi.org/10.1007/978-3-642-33042-1_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33041-4
Online ISBN: 978-3-642-33042-1
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