用张量网络解释异常数据，支持实数型输入和可解释性分析。
Explaining Anomalies with Tensor Networks
- 采用树状张量网络处理实值数据的异常检测
- 在三个基准任务中表现优于基线模型，准确识别异常样本
- 适合需要模型可解释性的安全与工业场景
张量网络作为一类变分量子多体波函数，在多个领域受到广泛关注，包括经典机器学习。近期，Aizpurua 等人利用矩阵乘积态在离散值网络安全任务中实现了可解释的异常检测，通过类量子方法揭示模型与异常的内在机制。本文将该框架拓展至实值数据领域，并引入树状张量网络用于可解释异常检测。我们在三个基准问题上验证了该方法的有效性，结果表明其预测性能与多个基线模型相当，且两种张量网络架构均具备解释异常样本的能力。本研究将张量网络的应用范围扩展至更广泛的潜在问题，并为未来向更复杂架构的延伸开辟路径。
原文摘要 · Abstract (English)
Tensor networks, a class of variational quantum many-body wave functions have attracted considerable research interest across many disciplines, including classical machine learning. Recently, Aizpurua et al. demonstrated explainable anomaly detection with matrix product states on a discrete-valued cyber-security task, using quantum-inspired methods to gain insight into the learned model and detected anomalies. Here, we extend this framework to real-valued data domains. We furthermore introduce tree tensor networks for the task of explainable anomaly detection. We demonstrate these methods with three benchmark problems, show adequate predictive performance compared to several baseline models and both tensor network architectures' ability to explain anomalous samples. We thereby extend the application of tensor networks to a broader class of potential problems and open a pathway for future extensions to more complex tensor network architectures.
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