Record link: 
http://hdl.handle.net/20.500.12708/120301
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Title: 
Outlier Detection in High Dimension Using Regularization
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Citation: 
Gschwandtner, M., & Filzmoser, P. (2013). Outlier Detection in High Dimension Using Regularization. In Synergies of Soft Computing and Statistics for Intelligent Data Analysis (pp. 237–244). Springer Verlag Berlin-Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_26
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Publisher DOI: 
10.1007/978-3-642-33042-1_26
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Gschwandtner, Moritz 
Filzmoser, Peter 
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Organisational Unit: 
E105-06 - Forschungsbereich Computational Statistics 
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Published in: 
Synergies of Soft Computing and Statistics for Intelligent Data Analysis 
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ISBN: 
978-3-642-33042-1
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DOI of the book: 
10.1007/978-3-642-33042-1
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Date (published): 
2013
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Event name: 
International Conference on Soft Methods in Probability and Statistics SMPS2012 
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Event date: 
4-Oct-2012 - 6-Oct-2012
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Event place: 
Konstanz / Germany, Austria
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Number of Pages: 
8
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Publisher: 
Springer Verlag Berlin-Heidelberg
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Publisher: 
Springer, Berlin, Heidelberg
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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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Research Areas: 
Mathematical and Algorithmic Foundations: 20%
Fundamental Mathematics Research: 50%
außerhalb der gesamtuniversitären Forschungsschwerpunkte: 30%
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Science Branch: 
Mathematik, Informatik
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Appears in Collections:
Conference Paper

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