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Dimension reduction for outlier detection in high-dimensional data
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DOI：10.1016/j.jmva.2025.105531.png)
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Abstract
En 中文
The work introduces the KASP (Kurtosis and Skewness Projections) procedure, a method for detecting outliers in high-dimensional multivariate data based on dimension reduction techniques. The procedure involves finding projections that maximize non-normality measures in the distribution of the observations. These projections are based on three directions: one that maximizes a combination of the squared skewness and kurtosis coefficients, one that minimizes the kurtosis coefficient, and one that maximizes the squared skewness coefficient. The study demonstrates that these directions include the optimal way to identify outliers for many different contamination structures. The performance of the KASP procedure is compared with alternative methods in correctly identifying and falsely detecting outliers in high-dimensional data sets. Additionally, the paper presents three practical examples to illustrate the effectiveness of the procedure in outlier detection in high dimensions.
Keywords:
Kurtosis
Projection pursuit
Robust statistics
Skewness
Third and fourth moments
Journal
J
IF:
1.7
Papers:
97
Citations:
5.8K