Book chapter
AD-DMKDE: Anomaly Detection Through Density Matrices and Fourier Features
Information Technology and Systems, pp 327-338
01 Jan 2023
Abstract
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.
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Details
- Title
- AD-DMKDE: Anomaly Detection Through Density Matrices and Fourier Features
- Creators
- Oscar A. Bustos-Brinez - Universidad Nacional de ColombiaJoseph A. Gallego-Mejia - Universidad Nacional de ColombiaFabio A. González - Universidad Nacional de Colombia
- Contributors
- Álvaro Rocha (Editor)Carlos Ferrás (Editor)Waldo Ibarra (Editor)
- Publication Details
- Information Technology and Systems, pp 327-338
- Series
- Lecture Notes in Networks and Systems
- Publisher
- Springer International Publishing; Cham
- Number of pages
- 12
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Scopus ID
- 2-s2.0-85169015909
- Other Identifier
- 991021916804404721