# Random Subspace Mixture Models for Interpretable Anomaly Detection

arXiv: https://arxiv.org/abs/2108.06283 · HTML: https://mlsanity.com/paper/2108.06283/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 13 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure](https://mlsanity.com/paper/2007.10088/) (2020)
- [DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles](https://mlsanity.com/paper/1911.10418/) (2019)
- [Bayesian Learning of Clique Tree Structure](https://mlsanity.com/paper/1708.07025/) (2017)
- [GALILEO: A Generalized Low-Entropy Mixture Model](https://mlsanity.com/paper/1708.07242/) (2017)

## Built on by (most cited first, 2 of 2)

- [A Survey on Explainable Anomaly Detection](https://mlsanity.com/paper/2210.06959/) (2022)
- [A Feature Engineering Approach for Business Impact-Oriented Failure Detection in Distributed Instant Payment Systems](https://mlsanity.com/paper/2510.21710/) (2025)

## Abstract (arXiv)

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.