基于最大相关熵准则的宽度学习系统
Broad Learning System Based on Maximum Correntropy Criterion
AI总结：
针对标准宽度学习系统（BLS）对异常值敏感的问题，提出基于最大相关熵准则的C-BLS方法及三种增量学习算法，在保持原性能的同时显著提升了系统的鲁棒性。
摘要：
作为一种高效且有效的判别式学习方法，宽度学习系统（BLS）因其在各种回归和分类问题中的出色表现而受到越来越多的关注。然而，标准的BLS是在最小均方误差（MMSE）准则下推导出来的，由于其对异常值敏感，这当然并不总是一个好的选择。为了增强BLS的鲁棒性，我们在本工作中提出采用最大相关熵准则（MCC）来训练输出权重，从而得到基于相关熵的宽度学习系统（C-BLS）。得益于MCC的固有优势，所提出的C-BLS有望在保持标准BLS在高斯或无噪声环境下的原始性能的同时，实现对异常值的出色鲁棒性。此外，我们还为C-BLS开发了三种可供选择的增量学习算法，这些算法源自加权正则化最小二乘解而非伪逆公式。借助增量学习算法，当有新样本到达或网络需要扩展时，系统可以快速更新，而无需从头开始进行整个重新训练过程。我们报告了在各种回归和分类数据集上的实验，以证明这些新方法的理想性能。
英文摘要：
As an effective and efficient discriminative learning method, Broad Learning System (BLS) has received increasing attention due to its outstanding performance in various regression and classification problems. However, the standard BLS is derived under the minimum mean square error (MMSE) criterion, which is, of course, not always a good choice due to its sensitivity to outliers. To enhance the robustness of BLS, we propose in this work to adopt the maximum correntropy criterion (MCC) to train the output weights, obtaining a correntropy based broad learning system (C-BLS). Thanks to the inherent superiorities of MCC, the proposed C-BLS is expected to achieve excellent robustness to outliers while maintaining the original performance of the standard BLS in Gaussian or noise-free environment. In addition, three alternative incremental learning algorithms, derived from a weighted regularized least-squares solution rather than pseudoinverse formula, for C-BLS are developed.With the incremental learning algorithms, the system can be updated quickly without the entire retraining process from the beginning, when some new samples arrive or the network deems to be expanded. Experiments on various regression and classification datasets are reported to demonstrate the desirable performance of the new methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
