Anomaly Detection With Infinite Set Dictionary Learning and Atom Dimension Adaptation

作     者：Baltoiu, Andra Ilie-Ablachim, Denis C. Dumitrescu, Bogdan 

作者机构：Natl Univ Sci & Technol POLITEHNICA Bucharest Fac Automat Control & Comp Bucharest 060042 Romania 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2025年第13卷

页      面：36233-36246页

核心收录：

基　　金：Ministry of Research  Innovation and Digitization  CNCS-UEFISCDI  within PNCDI III [PN-III-P4-PCE-2021-0154] 

主　　题：Atomic measurements Dictionaries Anomaly detection Standards Vectors Training Natural language processing Feature extraction Approximation algorithms Stress Dictionary learning cone atoms Gaussian atoms anomaly detection 

摘      要：Recent work on dictionary learning with set-atoms has shown benefits in anomaly detection. Instead of viewing an atom as a single vector, these methods allow building sparse representations with atoms taken from a set around a central vector;the set can be a cone or may have a probability distribution associated to it. We propose a method for adaptively adjusting the size of set-atoms in Gaussian and cone dictionary learning. The purpose of the algorithm is to match the atom sizes with their contribution in representing the signals. The proposed algorithm not only decreases the representation error, but also improves anomaly detection, for a class of anomalies called dependency . We obtain better detection performance than state-of-the-art methods.