提出高效在线异常检测方法，适应数据分布变化。
An Efficient Variant of One-Class SVM with Lifelong Online Learning Guarantees
- 基于SGD的OCSVM求解器，带强凸正则化
- 理论证明误差更小，支持长期学习适应变化
- 可配合变化点检测，应对恶意数据流
针对单次遍历非平稳流数据的异常检测问题，传统核One-Class SVM（OCSVM）计算量大且在分布变化时易产生大量漏报（Ⅱ型错误）。为此，我们提出SONAR——一种基于随机梯度下降（SGD）的OCSVM求解器，采用强凸正则化。理论证明，SONAR在Ⅰ/Ⅱ型错误上优于现有OCSVM；进一步证明其在温和分布偏移下具备良好的终身学习性能。在更具挑战性的对抗性非平稳数据中，将SONAR嵌入集成方法并结合变化点检测，可实现分段自适应保障，确保每阶段均保持低Ⅰ/Ⅱ型错误。在合成与真实数据集上验证了理论结果。
原文摘要 · Abstract (English)
We study outlier (a.k.a., anomaly) detection for single-pass non-stationary streaming data. In the well-studied offline or batch outlier detection problem, traditional methods such as kernel One-Class SVM (OCSVM) are both computationally heavy and prone to large false-negative (Type II) errors under non-stationarity. To remedy this, we introduce SONAR, an efficient SGD-based OCSVM solver with strongly convex regularization. We show novel theoretical guarantees on the Type I/II errors of SONAR, superior to those known for OCSVM, and further prove that SONAR ensures favorable lifelong learning guarantees under benign distribution shifts. In the more challenging problem of adversarial non-stationary data, we show that SONAR can be used within an ensemble method and equipped with changepoint detection to achieve adaptive guarantees, ensuring small Type I/II errors on each phase of data. We validate our theoretical findings on synthetic and real-world datasets.
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