使用随机子空间和子采样集成的 Dirichlet Process Mixtures 进行无监督离群点检测
Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process Mixtures
AI总结：
针对 Dirichlet Process Mixtures 计算低效且对非高斯数据建模困难的问题，提出一种结合随机子空间与子采样集成及变分推断的无监督离群点检测方法，有效提升了计算效率、鲁棒性和检测性能。
摘要：
概率混合模型因其可解释性和全局特征，被认为是无监督离群点检测的有效工具。其中，Dirichlet Process Mixture Models（Dirichlet过程混合模型）在聚类和离群点检测任务中，作为传统有限混合模型的强有力替代方案脱颖而出。与有限混合模型不同，Dirichlet Process Mixtures 是无限混合模型，能够基于数据自动确定混合成分的数量。尽管具备这些优势，但 Dirichlet Process Mixture Models 在无监督离群点检测中的应用一直受到计算效率低下以及在构建离群点检测器时对离群点敏感等挑战的限制。此外，Dirichlet Process Gaussian Mixtures 难以有效对具有离散或二元特征的非高斯数据进行建模。为了应对这些挑战，我们提出了一种利用 Dirichlet Process Gaussian Mixtures 集成的新型离群点检测方法。这种无监督算法采用随机子空间和子采样集成，以确保高效计算并提高离群点检测器的鲁棒性。集成方法进一步提升了所提方法在非高斯数据中检测离群点的适用性。此外，我们的方法对 Dirichlet Process Mixtures 使用变分推断，从而确保了高效且快速的计算。使用基准数据集进行的实证分析表明，我们的方法在无监督离群点检测中优于现有方法。
英文摘要：
Probabilistic mixture models are recognized as effective tools for unsupervised outlier detection owing to their interpretability and global characteristics. Among these, Dirichlet process mixture models stand out as a strong alternative to conventional finite mixture models for both clustering and outlier detection tasks. Unlike finite mixture models, Dirichlet process mixtures are infinite mixture models that automatically determine the number of mixture components based on the data. Despite their advantages, the adoption of Dirichlet process mixture models for unsupervised outlier detection has been limited by challenges related to computational inefficiency and sensitivity to outliers in the construction of outlier detectors. Additionally, Dirichlet process Gaussian mixtures struggle to effectively model non-Gaussian data with discrete or binary features. To address these challenges, we propose a novel outlier detection method that utilizes ensembles of Dirichlet process Gaussian mixtures. This unsupervised algorithm employs random subspace and subsampling ensembles to ensure efficient computation and improve the robustness of the outlier detector. The ensemble approach further improves the suitability of the proposed method for detecting outliers in non-Gaussian data. Furthermore, our method uses variational inference for Dirichlet process mixtures, which ensures both efficient and rapid computation. Empirical analyses using benchmark datasets demonstrate that our method outperforms existing approaches in unsupervised outlier detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
