Computer Science ›› 2022, Vol. 49 ›› Issue (8): 267-272.doi: 10.11896/jsjkx.210700175

• Artificial Intelligence • Previous Articles     Next Articles

KPCA Based Novelty Detection Method Using Maximum Correntropy Criterion

LI Qi-ye, XING Hong-jie   

  1. Hebei Key Laboratory of Machine Learning and Computational Intelligence,College of Mathematics and Information Science,Hebei University,Baoding,Hebei 071002,China
  • Received:2021-07-18 Revised:2022-02-28 Published:2022-08-02
  • About author:LI Qi-ye,born in 1995,postgraduate.His main research interests include novelty detection and kernel methods.
    XING Hong-jie,born in 1976,Ph.D,professor,master supervisor.His main research interests include kernel me-thods,neural networks,novelty detection and ensemble learning.
  • Supported by:
    National Natural Science Foundation of China(61672205), Natural Science Foundation of Hebei Province(F2017201020) and High-Level Talents Research Start-Up Project of Hebei University(521100222002).

Abstract: Novelty detection is an important research issue in the field of machine learning.Till now,there exist lots of novelty detection approaches.As a commonly used kernel method,kernel principal component analysis(KPCA)has been successfully applied to deal with the problem of novelty detection.However,the traditional KPCA based novelty detection method is very sensitive to noise.If there exist noise in the given training samples,the detection performance of KPCA based novelty detection method may be decreased.To enhance the anti-noise ability of KPCA based novelty detection method,a maximum correntropy criterion(MCC)based novelty detection method is proposed.Correntropy in information theoretic learning is utilized to substitute the 2-norm based measure in KPCA based novelty detection method.By adjusting the width parameter of the correntropy function,the adverse effect of noise can be alleviated.The half-quadratic optimization technique is used to solve the optimization problem of the proposed method.The local optimal solution can thus be obtained after a few iterations.Moreover,the algorithmic description of the proposed method is provided,and the computational complexity of the corresponding algorithm is analyzed.Experimental results on the 16 UCI benchmark data sets demonstrate that the proposed method obtains better anti-noise and generalization performance in comparison with the other four related approaches.

Key words: Correntropy, Half-quadratic optimization, Information theoretic learning, Kernel principal component analysis, Novelty detection

CLC Number: 

  • TP391.4
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