High-dimensional data poses unique challenges in outlier detection process. Most of the existing algorithms fail to properly address the issues stemming from a large number of features. In particular, outlier detection algorithms perform poorly on dataset of small size with a large number of features. In this paper, we propose a novel outlier detection algorithm based on principal component analysis and kernel density estimation. The proposed method is designed to address the challenges of dealing with high-dimensional data by projecting the original data onto a smaller space and using the innate structure of the data to calculate anomaly scores for each data point. Numerical experiments on synthetic and real-life data show that our method performs well on high-dimensional data. In particular, the proposed method outperforms the benchmark methods as measured by F1-score. Our method also produces better-than-average execution times compared with the benchmark methods.
Kamalov, F., & Leung, H. H. (2020). Outlier Detection in High Dimensional Data. Journal of Information and Knowledge Management, 19(1). https://doi.org/10.1142/S0219649220400134
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