Broad learning system with robust adaptive kernel
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914591434342400 |
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| author | Zhao, Haiquan Hu, Jinhui Lua, Xin |
| author_facet | Zhao, Haiquan Hu, Jinhui Lua, Xin |
| contents | For the performance degradation problem of broad learning system (BLS) in non-Gaussian noise environment, the variant of BLS based on M-estimator shows good robust performance. However, in most cases, the determination of the optimal loss function is often very time-consuming due to the lack of prior knowledge of the sample data. Therefore, this paper constructs a variant of BLS based on adaptive robust kernel (AR-BLS) to improve the generalization performance of the model in non-Gaussian noise environment. Adaptive robust kernel function is a general loss function that includes many common M-estimator paradigms. By alternately optimizing model weights and adaptive robust kernel parameters, AR-BLS realizes the adaptive adjustment of model robustness under different outlier noise distributions without human intervention. In addition, the iterative convergence of AR-BLS algorithm is proved based on Zangwill's global convergence theorem. Simulation experiments on multiple public datasets and actual application scenarios verify the effectiveness of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23495 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Broad learning system with robust adaptive kernel Zhao, Haiquan Hu, Jinhui Lua, Xin Signal Processing For the performance degradation problem of broad learning system (BLS) in non-Gaussian noise environment, the variant of BLS based on M-estimator shows good robust performance. However, in most cases, the determination of the optimal loss function is often very time-consuming due to the lack of prior knowledge of the sample data. Therefore, this paper constructs a variant of BLS based on adaptive robust kernel (AR-BLS) to improve the generalization performance of the model in non-Gaussian noise environment. Adaptive robust kernel function is a general loss function that includes many common M-estimator paradigms. By alternately optimizing model weights and adaptive robust kernel parameters, AR-BLS realizes the adaptive adjustment of model robustness under different outlier noise distributions without human intervention. In addition, the iterative convergence of AR-BLS algorithm is proved based on Zangwill's global convergence theorem. Simulation experiments on multiple public datasets and actual application scenarios verify the effectiveness of the proposed method. |
| title | Broad learning system with robust adaptive kernel |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.23495 |