KPCA Based Novelty Detection Method Using Maximum Correntropy Criterion

• Published in: Ji suan ji ke xue Vol. 49; no. 8; pp. 267 - 272
• Main Authors: Li, Qi-ye, Xing, Hong-jie
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01.08.2022; Editorial office of Computer Science
• Subjects: Algorithms; Criteria; Information theory; Kernel functions; kernel principal component analysis|correntropy|half-quadratic optimization|novelty detection|information theoretic learning; Machine learning; Noise sensitivity; Optimization; Optimization techniques; Principal components analysis
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.210700175

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 l