Robust Semi-supervised Learning Algorithm based on Maximum Correntropy Criterion
文献类型：期刊论文
| 作者 | Nanhai Yang; Mingming Huang; Ran He(赫然) ; Xiukun Wang
|
| 刊名 | Chinese Journal of Software
 |
| 出版日期 | 2012 |
| 卷号 | 23期号:2页码:279-288 |
| 关键词 | Semi-supervised Learning Gaussian-laplacian Regularized Correntropy Robust Half Quadratic Optimization |
| 英文摘要 | his 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/21165]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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