Fast Kernel-based method for anomaly detection

Anomaly detection (AD) involves detecting abnormality from normality and has a wide spectrum of applications in reality. Kernel-based methods for AD have been proven robust with diverse data distributions and offering good generalization ability. Stochastic gradient descent (SGD) method has recently...

Full description

Saved in:
Bibliographic Details
Published in2016 International Joint Conference on Neural Networks (IJCNN) pp. 3211 - 3217
Main Authors Anh Le, Trung Le, Khanh Nguyen, Van Nguyen, Thai Hoang Le, Dat Tran
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2016
Subjects
Anomaly detection
Computational modeling
Convergence
Kernel
Kernel method
Learning systems
Optimization
Stochastic algorithm
Support vector machines
Training
Online AccessGet full text

Cover

More Information
Summary:Anomaly detection (AD) involves detecting abnormality from normality and has a wide spectrum of applications in reality. Kernel-based methods for AD have been proven robust with diverse data distributions and offering good generalization ability. Stochastic gradient descent (SGD) method has recently emerged as a promising framework to devise ultra-fast learning methods. In this paper, we conjoin the advantages of Kernel-based method and SGD-based method to propose fast learning methods for anomaly detection. We validate the proposed methods on 8 benchmark datasets in UCI repository and KDD cup 1999 dataset. The experimental results show that the proposed methods offer a comparable one-class classification accuracy while simultaneously achieving a significantly computational speed-up.
ISSN:2161-4407
DOI:10.1109/IJCNN.2016.7727609