Outlier Detection in High Dimensional Data

Firuz Kamalov () and Ho Hon Leung ()
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Firuz Kamalov: Canadian University Dubai, Dubai, UAE
Ho Hon Leung: UAE University, UAE

Journal of Information & Knowledge Management (JIKM), 2020, vol. 19, issue 01, 1-16

Abstract: 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.

Keywords: Outlier detection; high dimensional data; PCA; KDE (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0219649220400134

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