Local Subspace-Based Outlier Detection using Global Neighbourhoods

TL;DR

GLOSS is a novel outlier detection algorithm that combines local subspace analysis with a global neighborhood perspective, improving detection accuracy in complex high-dimensional and mixed distribution data.

Contribution

It introduces GLOSS, a new method that explicitly incorporates global neighborhoods into local subspace outlier detection, addressing limitations of existing density-based algorithms.

Findings

GLOSS outperforms existing methods on synthetic data in detecting local outliers.

GLOSS identifies relevant outliers in real-world data that other methods overlook.

The global perspective enhances detection accuracy in complex, high-dimensional datasets.

Abstract

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.