RoseCDL高效检测长信号中的异常与罕见事件。
RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare event and Anomaly Detection
- 通过随机窗口和内嵌异常检测提升稳定性
- 在真实数据集上实现更高准确率和更快训练速度
- 适合大规模信号分析的无监督异常检测
在天文学、物理模拟和生物医学科学等领域，对大规模信号中的稀有事件和异常进行检测至关重要。许多情况下，该问题可自然分解为识别常见局部模式并检测偏离这些模式的异常。卷积字典学习（CDL）是建模局部结构的强大工具，但其应用受限于计算开销和对离群值的敏感性。我们提出RoseCDL，一种新型的CDL算法，用于鲁棒且可扩展地建模信号模式分布。RoseCDL采用随机窗口化策略实现高效训练，并引入在线异常检测机制增强鲁棒性。这使得无需标签即可基于局部重建误差，对长信号中的异常与罕见模式进行识别。在真实数据集上的实验表明，RoseCDL在检测准确率和计算效率方面均优于现有方法，使CDL在大规模信号分析的挑战性任务中更具实用性。
原文摘要 · Abstract (English)
Detecting rare events and anomalies in large-scale signals is essential in fields such as astronomy, physical simulations, and biomedical science. In many cases, this problem naturally decomposes into identifying common local patterns and detecting deviations that correspond to anomalies. Convolutional Dictionary Learning (CDL) is a powerful tool for modeling local structures, but its adoption for this task has been limited by computational demands and sensitivity to outliers. We introduce RoseCDL, a novel CDL algorithm designed for robust and scalable modeling of signal pattern distribution. RoseCDL leverages stochastic windowing for efficient training and incorporates inline outlier detection to enhance robustness. This enables unsupervised identification of anomalous and rare patterns in long signals based on the local reconstruction loss. Experiments on real-world datasets show that RoseCDL delivers improved detection accuracy and computational efficiency, making CDL practical for challenging detection tasks in large-scale signal analysis.
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