论文标题
Lean-DMKDE：量子潜在密度估计的异常检测
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection
论文作者
论文摘要
本文提出了一个异常检测模型，该模型结合了基于密度估计的异常检测方法的强统计基础与深度学习模型的表示能力。该方法结合了一个自动编码器，用于学习数据的低维表示，以及基于随机傅立叶特征和端到端体系结构中的密度估计模型，可以使用基于梯度的优化技术进行培训。该方法根据估计密度预测新样品的正态度。在不同的基准数据集上进行了系统的实验评估。实验结果表明，该方法在与其他最先进的方法相当或胜过方面的表现。
This paper presents an anomaly detection model that combines the strong statistical foundation of density-estimation-based anomaly detection methods with the representation-learning ability of deep-learning models. The method combines an autoencoder, for learning a low-dimensional representation of the data, with a density-estimation model based on random Fourier features and density matrices in an end-to-end architecture that can be trained using gradient-based optimization techniques. The method predicts a degree of normality for new samples based on the estimated density. A systematic experimental evaluation was performed on different benchmark datasets. The experimental results show that the method performs on par with or outperforms other state-of-the-art methods.
