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Outlier Detection in High Dimension Using Regularization

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Synergies of Soft Computing and Statistics for Intelligent Data Analysis, volume 190 of Advances in Intelligent Systems and Computing, Springer Berlin Heidelberg, (2013)
DOI: 10.1007/978-3-642-33042-1_26

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.

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