Distance-based outlier detection for high dimension, low sample size data

Jeongyoun Ahn, Myung Hee Lee and Jung Ae Lee

Journal of Applied Statistics, 2019, vol. 46, issue 1, 13-29

Abstract: Despite the popularity of high dimension, low sample size data analysis, there has not been enough attention to the sample integrity issue, in particular, a possibility of outliers in the data. A new outlier detection procedure for data with much larger dimensionality than the sample size is presented. The proposed method is motivated by asymptotic properties of high-dimensional distance measures. Empirical studies suggest that high-dimensional outlier detection is more likely to suffer from a swamping effect rather than a masking effect, thus yields more false positives than false negatives. We compare the proposed approaches with existing methods using simulated data from various population settings. A real data example is presented with a consideration on the implication of found outliers.

Date: 2019
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DOI: 10.1080/02664763.2018.1452901

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