Robust Anomaly Detection Using Semidefinite Programming

TL;DR

This paper introduces a novel anomaly detection method leveraging polynomial optimization and moments, which simplifies high-dimensional data analysis and outperforms traditional techniques like Parzen windows and 1-class SVM.

Contribution

It proposes a new anomaly detection approach based solely on statistical moments, offering a simpler and more effective solution for high-dimensional datasets.

Findings

Outperforms existing methods like Parzen windows and 1-class SVM

Provides a succinct description of the normal state

Simplifies anomaly detection in high-dimensional data

Abstract

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.