Robust Semi-Supervised Learning Algorithm Based on Maximum Correntropy Criterion

Abstract

PDF HTML阅读 XML下载 导出引用 引用提醒 基于最大相关熵准则的鲁棒半监督学习算法 DOI: 10.3724/SP.J.1001.2012.03977 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(60873054, 50909012); 国家教育部高等学校博士学科点专项科研基金(20100041120009) Robust Semi-Supervised Learning Algorithm Based on Maximum Correntropy Criterion Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:分析了噪声对半监督学习Gaussian-Laplacian 正则化(Gaussian-Laplacian regularized,简称GLR)框架的影响,针对最小二乘准则对噪声敏感的特点,结合信息论的最大相关熵准则(maximum correntropy criterion,简称MCC), 提出了一种基于最大相关熵准则的鲁棒半监督学习算法(简称GLR-MCC),并证明了算法的收敛性.半二次优化技术被用来求解相关熵目标函数.在每次迭代中,复杂的信息论优化问题被简化为标准的半监督学习问题.典型机器学习数据集上的仿真实验结果表明,在标签噪声和遮挡噪声的情况下,该算法能够有效地提高半监督学习算法性能. Abstract:This 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. 参考文献 相似文献 引证文献