Broad Learning System Based on Maximum Correntropy Criterion
This work addresses robustness issues in BLS for regression and classification tasks, but it is incremental as it adapts an existing criterion to a known method.
The authors tackled the problem of Broad Learning System (BLS) being sensitive to outliers by proposing a correntropy-based variant (C-BLS) using the maximum correntropy criterion, which demonstrated improved robustness to outliers while maintaining performance in noise-free environments, with experiments on various datasets.
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.