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.

翻译：本文提出了一种通过动态$\ell$p-范数约束下的局部自适应学习实现单分类器融合的新方法。我们引入了一个框架，能够根据局部数据特征动态调整融合权重，以解决基于集成的异常检测中的基本挑战。与传统Frank-Wolfe方法相比，我们的方法采用了一种内点优化技术，显著提高了计算效率，在复杂场景下实现了高达19倍的速度提升。该框架在标准UCI基准数据集和专用时间序列数据集上进行了广泛评估，结果表明其在多种异常类型上均表现出优越性能。通过Skillings-Mack检验进行的统计验证证实，我们的方法相较于现有方法具有显著优势，在纯学习与非纯学习场景下均保持一致的顶尖排名。该框架能够在保持计算效率的同时适应局部数据模式，使其在需要快速准确异常检测的实时应用中具有重要价值。