AD-DMKDE: Anomaly Detection through Density Matrices and Fourier Features
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.
Code (1)
Tasks
Anomaly DetectionDensity EstimationGPUSimilar Papers 제목 키워드 기반
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection
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 m…
Anomaly DetectionDensity EstimationRepresentation LearningFast Kernel Density Estimation with Density Matrices and Random Fourier Features
Kernel density estimation (KDE) is one of the most widely used nonparametric density estimation methods. The fact that it is a memory-based method, i.e., it uses the entire training data set for prediction, makes it unsu…
Density EstimationLatent Anomaly Detection Through Density Matrices
This paper introduces a novel anomaly detection framework that combines the robust statistical principles of density-estimation-based anomaly detection methods with the representation-learning capabilities of deep learni…
Anomaly DetectionDensity EstimationRepresentation LearningInQMAD: Incremental Quantum Measurement Anomaly Detection
Streaming anomaly detection refers to the problem of detecting anomalous data samples in streams of data. This problem poses challenges that classical and deep anomaly detection methods are not designed to cope with, suc…
Anomaly DetectionDensity EstimationAnomaly detection in reconstructed quantum states using a machine-learning technique
The accurate detection of small deviations in given density matrices is important for quantum information processing. Here we propose a new method based on the concept of data mining. We demonstrate that the proposed met…
Anomaly DetectionBIG-bench Machine Learning