Outlier detection in high-dimensional data is a challenging yet important
task, as it has applications in, e.g., fraud detection and quality control.
State-of-the-art density-based algorithms perform well because they 1) take the
local neighbourhoods of data points into account and 2) consider feature
subspaces. In highly complex and high-dimensional data, however, existing
methods are likely to overlook important outliers because they do not
explicitly take into account that the data is often a mixture distribution of
multiple components.
We therefore introduce GLOSS, an algorithm that performs local subspace
outlier detection using global neighbourhoods. Experiments on synthetic data
demonstrate that GLOSS more accurately detects local outliers in mixed data
than its competitors. Moreover, experiments on real-world data show that our
approach identifies relevant outliers overlooked by existing methods,
confirming that one should keep an eye on the global perspective even when
doing local outlier detection.

本文提出了一种名为 GLOSS 的新算法，它在局部子空间之前使用全局邻域来检测高维复杂数据中的局部异常值，得到了比现有算法更准确的结果。