Broad Learning System Based on Maximum Correntropy Criterion

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,...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 32; číslo 7; s. 3083 - 3097
Hlavní autoři: Zheng, Yunfei, Chen, Badong, Wang, Shiyuan, Wang, Weiqun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Broad learning system (BLS)
Classification
Criteria
incremental learning algorithms
Learning algorithms
Learning systems
Machine learning
maximum correntropy criterion (MCC)
Mean square error methods
Noise standards
Optimization
Outliers (statistics)
Perturbation methods
regression and classification
Robustness
Sensitivity enhancement
Training
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí: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 BLS (C-BLS). Due 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 the 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 data sets are reported to demonstrate the desirable performance of the new methods.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2020.3009417