动态调整权重，提升异常检测的准确与速度。
Locally Adaptive One-Class Classifier Fusion with Dynamic $\ell$p-Norm Constraints for Robust Anomaly Detection
- 根据局部数据特征自适应调整分类器融合权重
- 复杂场景下比传统方法快19倍，性能更优
- 适合实时异常检测，尤其对时序数据有效
本文提出一种基于局部自适应学习与动态ℓp-范数约束的一类分类器融合新方法。通过引入内点优化技术，显著提升计算效率，相比传统Frank-Wolfe方法在复杂场景下最高提速19倍。该框架在标准UCI基准数据集及专用时序数据集上广泛评估，对多种异常类型均表现优异。技能林-麦克检验表明，本方法在纯学习与非纯学习场景中均显著优于现有方法，排名始终领先。其既能适应局部数据模式，又保持高效计算，特别适用于对快速准确异常检测要求高的实时应用。
原文摘要 · Abstract (English)
This paper presents a novel approach to one-class classifier fusion through locally adaptive learning with dynamic $\ell$p-norm constraints. We introduce a framework that dynamically adjusts fusion weights based on local data characteristics, addressing fundamental challenges in ensemble-based anomaly detection. Our method incorporates an interior-point optimization technique that significantly improves computational efficiency compared to traditional Frank-Wolfe approaches, achieving up to 19-fold speed improvements in complex scenarios. The framework is extensively evaluated on standard UCI benchmark datasets and specialized temporal sequence datasets, demonstrating superior performance across diverse anomaly types. Statistical validation through Skillings-Mack tests confirms our method's significant advantages over existing approaches, with consistent top rankings in both pure and non-pure learning scenarios. The framework's ability to adapt to local data patterns while maintaining computational efficiency makes it particularly valuable for real-time applications where rapid and accurate anomaly detection is crucial.
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