Robust Anomaly Detection Using Semidefinite Programming

Jose A. Lopez; Octavia Camps; Mario Sznaier

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arXiv · 1504.00905

Robust Anomaly Detection Using Semidefinite Programming

Jose A. Lopez·Octavia Camps·Mario Sznaier

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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.

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BibTeX RIS

Jose A. Lopez, Octavia Camps, Mario Sznaier. 2015-04-03. Robust Anomaly Detection Using Semidefinite Programming. https://arxiv.org/abs/1504.00905

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