用随机PCA森林做无监督异常检测，速度快效果好。
Randomized PCA Forest for Unsupervised Outlier Detection
- 基于随机PCA森林构建异常评分机制
- 在多个数据集上优于经典与先进方法
- 适合大规模高效异常检测场景
我们提出一种基于随机主成分分析（RPCA）的新型无监督异常检测方法。受随机PCA森林在近似最近邻搜索中的表现启发，本文利用其内在特性构建异常评分，实现无监督异常检测。实验表明，该方法在多个数据集上的异常检测任务中优于经典及前沿方法，其余数据集上表现也具有竞争力。对所提方法的广泛分析显示其具备鲁棒性与计算高效性，是无监督异常检测的可靠选择。
原文摘要 · Abstract (English)
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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