Outlier Detection in High Dimension Using Regularization
M. Gschwandtner, и P. Filzmoser. Synergies of Soft Computing and Statistics for Intelligent Data Analysis, том 190 из Advances in Intelligent Systems and Computing, Springer Berlin Heidelberg, (2013)
DOI: 10.1007/978-3-642-33042-1_26
Аннотация
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.
Описание
Outlier Detection in High Dimension Using Regularization - Springer
%0 Book Section
%1 gschwandtner2013outlier
%A Gschwandtner, Moritz
%A Filzmoser, Peter
%B Synergies of Soft Computing and Statistics for Intelligent Data Analysis
%D 2013
%E Kruse, Rudolf
%E Berthold, Michael R.
%E Moewes, Christian
%E Gil, María Ángeles
%E Grzegorzewski, Przemysław
%E Hryniewicz, Olgierd
%I Springer Berlin Heidelberg
%K multivariate outliers r robust statistics
%P 237-244
%R 10.1007/978-3-642-33042-1_26
%T Outlier Detection in High Dimension Using Regularization
%U http://dx.doi.org/10.1007/978-3-642-33042-1_26
%V 190
%X 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.
%@ 978-3-642-33041-4
@incollection{gschwandtner2013outlier,
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.},
added-at = {2013-03-03T17:57:02.000+0100},
author = {Gschwandtner, Moritz and Filzmoser, Peter},
biburl = {https://www.bibsonomy.org/bibtex/2bb80c1f3da813e2c4d1eb247b9aeebb5/vivion},
booktitle = {Synergies of Soft Computing and Statistics for Intelligent Data Analysis},
description = {Outlier Detection in High Dimension Using Regularization - Springer},
doi = {10.1007/978-3-642-33042-1_26},
editor = {Kruse, Rudolf and Berthold, Michael R. and Moewes, Christian and Gil, María Ángeles and Grzegorzewski, Przemysław and Hryniewicz, Olgierd},
interhash = {c1b70e624945b3060f9543319d3eaca1},
intrahash = {bb80c1f3da813e2c4d1eb247b9aeebb5},
isbn = {978-3-642-33041-4},
keywords = {multivariate outliers r robust statistics},
pages = {237-244},
publisher = {Springer Berlin Heidelberg},
series = {Advances in Intelligent Systems and Computing},
timestamp = {2013-03-03T17:57:03.000+0100},
title = {Outlier Detection in High Dimension Using Regularization},
url = {http://dx.doi.org/10.1007/978-3-642-33042-1_26},
volume = 190,
year = 2013
}