LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection
This addresses anomaly detection problems for applications needing robust statistical methods, though it appears incremental as it combines existing techniques.
The paper tackled anomaly detection by combining autoencoder representation learning with density estimation using random Fourier features and density matrices in an end-to-end trainable architecture, achieving performance on par with or better than state-of-the-art methods on benchmark datasets.
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.