Batch quadratic programming network with maximum entropy constraint for anomaly detection | Zendy

Zhou Di | Zendy; Chen Weigang | Zendy; Guo Chunsheng | Zendy; Zhang Mark | Zendy

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Batch quadratic programming network with maximum entropy constraint for anomaly detection

Author(s) -

Zhou Di,

Chen Weigang,

Guo Chunsheng,

Zhang Mark

Publication year - 2022

Publication title -

iet computer vision

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.38

H-Index - 37

eISSN - 1751-9640

pISSN - 1751-9632

DOI - 10.1049/cvi2.12082

Subject(s) - anomaly detection , principle of maximum entropy , quadratic programming , computer science , constraint (computer aided design) , quadratic equation , constraint programming , artificial intelligence , entropy (arrow of time) , mathematical optimization , pattern recognition (psychology) , mathematics , stochastic programming , physics , geometry , quantum mechanics

The difficulty of anomaly detection lies in balancing the impact of noise on the network (noise suppression) and distinguishing the real anomaly from the noise (abnormal exposure). So, a deep anomaly detector Batch Quadratic Programming (BQP) network with Maximum Entropy Constraint is proposed. It imposes quadratic programming constraints on Support Vector Data Description through the BQP output layer to achieve noise suppression. In BQP network processes’ batch data, Maximum Entropy Constraint is used to balance abnormal samples and noise. The experiment compared the shallow method with the currently popular deep method on MNIST and CIFAR‐10 data sets and proved that the BQP network with Maximum Entropy Constraint has excellent performance.

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