2015/04/03 by Jose A. Lopez, Lopez, Jose A., Octavia Camps +3
Computer Science · Decision Sciences · Mathematics · #Advanced Optimization Algorithms Research #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning and Algorithms #Optimization and Control (math.OC) #Probabilistic and Robust Engineering Design #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1504.00905
openalex publication_date 2015/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.