  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 data set 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 the $F_1$-score. Our method
also produces better-than-average execution times compared to the benchmark
methods.
