A Comparison of Outlier Detection Techniques for High-Dimensional Data

Outlier detection is a hot topic in machine learning.With the newly emerging technologies and diverse applications, the interest of outlier detection is increasing greatly.Recently, a significant number of outlier detection methods have been witnessed and successfully applied in a wide range of fields, including medical health, credit card fraud and intrusion detection.They can be used for conventional data analysis.However, it is not a trivial work to identify rare behaviors or patterns out from complicated data.In this paper, we provide a brief overview of the outlier detection methods for high-dimensional data, and offer comprehensive understanding of the-state-of-the-art techniques of outlier detection for practitioners.Specifically, we firstly summarize the recent advances on outlier detection for high-dimensional data, and then make an extensive experimental comparison to the popular detection methods on public datasets.Finally, several challenging issues and future research directions are discussed.