A Comparison of Outlier Detection Algorithms for Machine Learning

Abstract

In this paper a comparison of outlier detection algorithms is presented, we present an overview on outlier detection methods and experimental results of six implemented methods. We applied these methods for the prediction of stellar populations parameters as well as on machine learning benchmark data, inserting artificial noise and outliers. We used kernel principal component analysis in order to reduce the dimensionality of the spectral data. Experiments on noisy and noiseless data were performed.