论文标题
AD-DMKDE：通过密度矩阵和傅立叶特征的异常检测
AD-DMKDE: Anomaly Detection through Density Matrices and Fourier Features
论文作者
论文摘要
本文提出了一种使用密度矩阵（来自量子力学的强大数学形式主义）和傅立叶特征的新型密度估计方法。该方法可以看作是内核密度估计（KDE）的有效近似。提出了对各种数据集的11个最先进的异常检测方法的系统比较，显示了不同基准数据集的竞争性能。该方法是有效训练的，并使用优化来找到数据嵌入的参数。相对于训练数据大小，所提出的算法的预测阶段复杂性是恒定的，并且在不同异常速率的数据集中表现良好。它的体系结构允许向量化，可以在GPU/TPU硬件上实现。
This paper presents a novel density estimation method for anomaly detection using density matrices (a powerful mathematical formalism from quantum mechanics) and Fourier features. The method can be seen as an efficient approximation of Kernel Density Estimation (KDE). A systematic comparison of the proposed method with eleven state-of-the-art anomaly detection methods on various data sets is presented, showing competitive performance on different benchmark data sets. The method is trained efficiently and it uses optimization to find the parameters of data embedding. The prediction phase complexity of the proposed algorithm is constant relative to the training data size, and it performs well in data sets with different anomaly rates. Its architecture allows vectorization and can be implemented on GPU/TPU hardware.
