Robust Anomaly Detection Using Semidefinite Programming
It simplifies anomaly detection for higher-dimensional datasets, though it appears incremental as it builds on known techniques.
The paper tackles anomaly detection by using polynomial optimization and the method of moments, requiring only statistical moments of normal-state features, and shows favorable performance compared to existing methods like Parzen windows and 1-class SVM.
This paper presents a new approach, based on polynomial optimization and the method of moments, to the problem of anomaly detection. The proposed technique only requires information about the statistical moments of the normal-state distribution of the features of interest and compares favorably with existing approaches (such as Parzen windows and 1-class SVM). In addition, it provides a succinct description of the normal state. Thus, it leads to a substantial simplification of the the anomaly detection problem when working with higher dimensional datasets.