arXiv 2508.127762026-05-08cs.LGcs.AIstat.ML

基于随机PCA森林的无监督异常检测方法

Randomized PCA Forest for Unsupervised Outlier Detection

Department of Mathematics and Computer Science（数学与计算机科学系）；University of Southern Denmark（南丹麦大学）；Faculty of Information Technology and Communication Sciences（信息科技与通信科学学院）；Tampere University（塔尔库大学）

Muhammad Rajabinasab, Farhad Pakdaman, Moncef Gabbouj, Peter Schneider-Kamp, Arthur Zimek

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AI总结：

本文提出一种基于随机PCA的无监督异常检测方法，利用RPCA森林的内在特性生成异常评分，在多个数据集上优于经典和最新方法，展现出鲁棒性和高效性。

摘要：

我们提出了一种基于随机主成分分析（PCA）的新型无监督异常检测方法。受随机PCA（RPCA）森林在近邻搜索中的性能启发，我们开发了一种利用RPCA森林进行无监督异常检测的新方法，通过其内在特性推导出异常评分。实验结果展示了所提方法在多个数据集上的优越性，同时在其他数据集上表现竞争。对所提方法的广泛分析显示其鲁棒性和计算效率，使其成为无监督异常检测的良好选择。

英文摘要：

We propose a novel unsupervised outlier detection method based on Randomized Principal Component Analysis (PCA). Motivated by the performance of Randomized PCA (RPCA) Forest in approximate K-Nearest Neighbor (KNN) search, we develop a novel unsupervised outlier detection method that utilizes RPCA Forest for unsupervised outlier detection by deriving an outlier score from its intrinsic properties. Experimental results showcase the superiority of the proposed approach compared to the classical and state-of-the-art methods in performing the outlier detection task on several datasets while performing competitively on the rest. The extensive analysis of the proposed method reflects its robustness and its computational efficiency, highlighting it as a good choice for unsupervised outlier detection.

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