A Regularized Correntropy Framework for Robust Pattern Recognition
A Regularized Correntropy Framework for Robust Pattern Recognition
复制标题
鲁棒模式识别的正则化熵框架
DOI：
10.1162/neco_a_00155
复制
发表时间：
2011-08-01
影响因子：
2.9
通讯作者：
Kong, Xiang-Wei
中科院分区：
文献类型：
--
作者：
He, Ran;Zheng, Wei-Shi;Kong, Xiang-Wei
本文提出了一种新的基于正则化相关熵的多元线性回归模型，用于鲁棒模式识别。首先，我们鼓励使用相关熵来提高经典的均方误差（MSE）标准，是敏感的离群值的鲁棒性。然后对相关熵施加l1正则化方案以学习鲁棒和稀疏表示。基于半二次优化技术，我们提出了一种新的算法来解决非线性优化问题。其次，我们基于学习到的正则化方案开发了一种新的基于相关性的分类器，用于鲁棒的对象识别。大量的应用实验证实，基于相关性的l1正则化可以提高识别精度和接收机运营商的特性曲线在噪声污染和遮挡。
This letter proposes a new multiple linear regression model using regularized correntropy for robust pattern recognition. First, we motivate the use of correntropy to improve the robustness of the classical mean square error (MSE) criterion that is sensitive to outliers. Then an l1 regularization scheme is imposed on the correntropy to learn robust and sparse representations. Based on the half-quadratic optimization technique, we propose a novel algorithm to solve the nonlinear optimization problem. Second, we develop a new correntropy-based classifier based on the learned regularization scheme for robust object recognition. Extensive experiments over several applications confirm that the correntropy-based l1 regularization can improve recognition accuracy and receiver operator characteristic curves under noise corruption and occlusion.