AD-DMKDE: Anomaly Detection through Density Matrices and Fourier Features
This work addresses anomaly detection for data analysis applications, but it is incremental as it builds on existing density estimation techniques with efficiency improvements.
The paper tackled anomaly detection by proposing a density estimation method using density matrices and Fourier features, which approximates Kernel Density Estimation efficiently, and showed competitive performance compared to eleven state-of-the-art methods on various benchmark datasets.
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.