Outlier Detection in High Dimension Using Regularization

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.