Restricted Minimum Error Entropy Criterion for Robust Classification
This work addresses robust classification for machine learning applications with outliers, but it is incremental as it modifies an existing criterion for a specific problem.
The authors tackled robust classification in noisy environments by proposing a restricted minimum error entropy (RMEE) criterion, which improved classification accuracy by up to 15% compared to standard methods in experiments with logistic regression and extreme learning machine.
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.