受限最小误差熵准则用于鲁棒分类
Restricted Minimum Error Entropy Criterion for Robust Classification
AI总结：
针对最小误差熵在鲁棒分类中的空白，提出受限最小误差熵准则，通过码本限制误差分布，结合半二次优化，提升分类鲁棒性。
摘要：
最小误差熵（MEE）准则已被验证为非高斯信号处理和鲁棒机器学习中的一种强大方法。然而，MEE在鲁棒分类中的应用在文献中尚属空白。原始的MEE仅关注最小化误差概率密度函数（PDF）的Renyi二次熵，这可能导致在噪声分类任务中失效。为此，我们分析了在存在离群点的情况下，具有连续误差的分类器的最优误差分布，并引入一个简单的码本来限制MEE，使其驱动误差PDF趋向期望情形。提供了基于半二次的优化以及新学习准则（称为受限MEE，RMEE）的收敛性分析。通过逻辑回归和极限学习机的实验结果验证了RMEE的理想鲁棒性。
英文摘要：
The minimum error entropy (MEE) criterion has been verified as a powerful approach for non-Gaussian signal processing and robust machine learning. However, the implementation of MEE on robust classification is rather a vacancy in the literature. The original MEE only focuses on minimizing the Renyi's quadratic entropy of the error probability distribution function (PDF), which could cause failure in noisy classification tasks. To this end, we analyze the optimal error distribution in the presence of outliers for those classifiers with continuous errors, and introduce a simple codebook to restrict MEE so that it drives the error PDF towards the desired case. Half-quadratic based optimization and convergence analysis of the new learning criterion, called restricted MEE (RMEE), are provided. Experimental results with logistic regression and extreme learning machine are presented to verify the desirable robustness of RMEE.
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