用凸包体积变化检测异常数据，效果媲美顶尖方法。
Tighten The Lasso: A Convex Hull Volume-based Anomaly Detection Method
- 通过移除样本并监测凸包体积变化，构建异常判定边界。
- 在10个数据集上表现接近当前最优，7种对比方法中优势明显。
- 提供高效判别标准，快速识别适合该方法的数据集。
检测分布外（OOD）数据是保障模型可靠性与鲁棒性的关键任务。本文提出一种新型异常检测算法，利用数据集的凸包（CH）特性：相比分布内样本，分布外样本对凸包体积的增加微乎其微。通过迭代移除样本并计算凸包体积变化，当移除不再显著影响体积时停止，从而建立分布内外的决策边界。该方法在10个数据集上与七种主流异常检测方法对比，性能接近当前最优水平。此外，我们还提出一种计算高效的判别准则，用于识别该方法优于现有最优技术的数据集。
原文摘要 · Abstract (English)
Detecting out-of-distribution (OOD) data is a critical task for maintaining model reliability and robustness. In this study, we propose a novel anomaly detection algorithm that leverages the convex hull (CH) property of a dataset by exploiting the observation that OOD samples marginally increase the CH's volume compared to in-distribution samples. Thus, we establish a decision boundary between OOD and in-distribution data by iteratively computing the CH's volume as samples are removed, stopping when such removal does not significantly alter the CH's volume. The proposed algorithm is evaluated against seven widely used anomaly detection methods across ten datasets, demonstrating performance comparable to state-of-the-art (SOTA) techniques. Furthermore, we introduce a computationally efficient criterion for identifying datasets where the proposed method outperforms existing SOTA approaches.
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