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Robust Semi-supervised Learning Algorithm based on Maximum Correntropy Criterion
Nanhai Yang; Mingming Huang; Ran He(赫然); Xiukun Wang
Source PublicationChinese Journal of Software
2012
Volume23Issue:2Pages:279-288
Abstracthis paper analyzes the problem of sensitivity to noise in the mean square criterion of Gaussian- Laplacian regularized (GLR) algorithm. A robust semi-supervised learning algorithm based on maximum correntropy criterion (MCC), called GLR-MCC, is proposed to improve the robustness of GLR along with its convergence analysis. The half quadratic optimization technique is used to simplify the correntropy optimization problem to a standard semi-supervised problem in each iteration. Experimental results on typical machine learning data sets show that the proposed GLR-MCC can effectively improve the robustness of mislabeling noise and occlusion as compared with related semi-supervised learning algorithms.
KeywordSemi-supervised Learning Gaussian-laplacian Regularized Correntropy Robust Half Quadratic Optimization
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21165
Collection智能感知与计算研究中心
Recommended Citation
GB/T 7714
Nanhai Yang,Mingming Huang,Ran He,et al. Robust Semi-supervised Learning Algorithm based on Maximum Correntropy Criterion[J]. Chinese Journal of Software,2012,23(2):279-288.
APA Nanhai Yang,Mingming Huang,Ran He,&Xiukun Wang.(2012).Robust Semi-supervised Learning Algorithm based on Maximum Correntropy Criterion.Chinese Journal of Software,23(2),279-288.
MLA Nanhai Yang,et al."Robust Semi-supervised Learning Algorithm based on Maximum Correntropy Criterion".Chinese Journal of Software 23.2(2012):279-288.
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