VSCOUT：一种混合变分自编码器方法用于高维回顾性监控中的异常检测
VSCOUT: A Hybrid Variational Autoencoder Approach to Outlier Detection in High-Dimensional Retrospective Monitoring
AI总结：
VSCOUT通过结合变分自编码器和集成过滤技术，提升高维回顾性监控中异常检测的灵敏度和鲁棒性。
摘要：
现代工业和服务过程生成的高维、非高斯分布且易受污染的数据挑战了经典统计过程控制（SPC）的基础假设。重尾分布、多模态、非线性依赖性和稀疏特殊原因观测可以扭曲基线估计，掩盖真实异常并阻止可靠识别受控（IC）参考集。为了解决这些挑战，我们引入了VSCOUT，一种无分布框架，专门针对高维设置中的回顾性（Phase I）监控。VSCOUT结合了自动相关性确定变分自编码器（ARD-VAE）架构与基于集成的潜在异常过滤和突变点检测。ARD先验隔离了最有信息的潜在维度，而集成和突变点过滤器在确定的潜在空间中识别点状和结构性污染。第二阶段的重新训练步骤移除被标记的观测并仅使用保留的内群观测重新估计潜在结构，从而缓解遮蔽并稳定IC潜在流形。这种两阶段细化产生了一个干净且可靠的IC基线，适用于后续的Phase II部署。在基准数据集上的广泛实验表明，VSCOUT在检测特殊原因结构方面具有优越的灵敏度，同时保持可控的误报率，优于经典SPC程序、稳健估计器和现代机器学习基线。其可扩展性、分布灵活性和对复杂污染模式的抗性使VSCOUT成为在AI赋能环境中进行回顾建模和异常检测的实用且有效的方法。
英文摘要：
Modern industrial and service processes generate high-dimensional, non-Gaussian, and contamination-prone data that challenge the foundational assumptions of classical Statistical Process Control (SPC). Heavy tails, multimodality, nonlinear dependencies, and sparse special-cause observations can distort baseline estimation, mask true anomalies, and prevent reliable identification of an in-control (IC) reference set. To address these challenges, we introduce VSCOUT, a distribution-free framework designed specifically for retrospective (Phase I) monitoring in high-dimensional settings. VSCOUT combines an Automatic Relevance Determination Variational Autoencoder (ARD-VAE) architecture with ensemble-based latent outlier filtering and changepoint detection. The ARD prior isolates the most informative latent dimensions, while the ensemble and changepoint filters identify pointwise and structural contamination within the determined latent space. A second-stage retraining step removes flagged observations and re-estimates the latent structure using only the retained inliers, mitigating masking and stabilizing the IC latent manifold. This two-stage refinement produces a clean and reliable IC baseline suitable for subsequent Phase II deployment. Extensive experiments across benchmark datasets demonstrate that VSCOUT achieves superior sensitivity to special-cause structure while maintaining controlled false alarms, outperforming classical SPC procedures, robust estimators, and modern machine-learning baselines. Its scalability, distributional flexibility, and resilience to complex contamination patterns position VSCOUT as a practical and effective method for retrospective modeling and anomaly detection in AI-enabled environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
