Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process Mixtures

Probabilistic mixture models are acknowledged as a valuable tool for unsupervised outlier detection owing to their interpretability and intuitive grounding in statistical principles. Within this framework, Dirichlet process mixture models emerge as a compelling alternative to conventional finite mixture models for both clustering and outlier detection tasks. However, despite their evident advantages, the widespread adoption of Dirichlet process mixture models in unsupervised outlier detection has been hampered by challenges related to computational inefficiency and sensitivity to outliers during the construction of detectors. To tackle these challenges, we propose a novel outlier detection method based on ensembles of Dirichlet process Gaussian mixtures. The proposed method is a fully unsupervised algorithm that capitalizes on random subspace and subsampling ensembles, not only ensuring efficient computation but also enhancing the robustness of the resulting outlier detector. Moreover, the proposed method leverages variational inference for Dirichlet process mixtures to ensure efficient and fast computation. Empirical studies with benchmark datasets demonstrate that our method outperforms existing approaches for unsupervised outlier detection.