Robust Semi-Supervised Learning Algorithm Based on Maximum Correntropy Criterion
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    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.

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杨南海,黄明明,赫然,王秀坤.基于最大相关熵准则的鲁棒半监督学习算法.软件学报,2012,23(2):279-288

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History
  • Received:May 04,2010
  • Revised:October 11,2010
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  • Online: February 07,2012
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