Random Subspace Mixture Models for Interpretable Anomaly Detection

TL;DR

This paper introduces a novel subspace-based probabilistic model for high-dimensional anomaly detection that combines random subspace densities with geometric averaging, emphasizing interpretability and scalability.

Contribution

It proposes a new method using random subspaces and Gaussian mixture models for anomaly detection, ensuring interpretability and automatic component selection.

Findings

Achieves competitive AUC scores on benchmark datasets.

Simple, scalable, and interpretable approach.

Effectively handles numerical and categorical data.

Abstract

We present a new subspace-based method to construct probabilistic models for high-dimensional data and highlight its use in anomaly detection. The approach is based on a statistical estimation of probability density using densities of random subspaces combined with geometric averaging. In selecting random subspaces, equal representation of each attribute is used to ensure correct statistical limits. Gaussian mixture models (GMMs) are used to create the probability densities for each subspace with techniques included to mitigate singularities allowing for the ability to handle both numerical and categorial attributes. The number of components for each GMM is determined automatically through Bayesian information criterion to prevent overfitting. The proposed algorithm attains competitive AUC scores compared with prominent algorithms against benchmark anomaly detection datasets with the added benefits of being simple, scalable, and interpretable.