Shen Jiuru, Zhao Huimin, Deng Wu
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China.
Sensors (Basel). 2024 Jun 30;24(13):4268. doi: 10.3390/s24134268.
The Broad Learning System (BLS) has demonstrated strong performance across a variety of problems. However, BLS based on the Minimum Mean Square Error (MMSE) criterion is highly sensitive to label noise. To enhance the robustness of BLS in environments with label noise, a function called Logarithm Kernel (LK) is designed to reweight the samples for outputting weights during the training of BLS in order to construct a Logarithm Kernel-based BLS (L-BLS) in this paper. Additionally, for image databases with numerous features, a Mixture Autoencoder (MAE) is designed to construct more representative feature nodes of BLS in complex label noise environments. For the MAE, two corresponding versions of BLS, MAEBLS, and L-MAEBLS were also developed. The extensive experiments validate the robustness and effectiveness of the proposed L-BLS, and MAE can provide more representative feature nodes for the corresponding version of BLS.
广义学习系统（BLS）在各种问题上都表现出了强大的性能。然而，基于最小均方误差（MMSE）准则的BLS对标签噪声高度敏感。为了提高BLS在存在标签噪声环境中的鲁棒性，本文设计了一种名为对数核（LK）的函数，用于在BLS训练期间对样本进行重新加权以输出权重，从而构建基于对数核的BLS（L-BLS）。此外，对于具有众多特征的图像数据库，设计了一种混合自动编码器（MAE），以在复杂的标签噪声环境中构建更具代表性的BLS特征节点。对于MAE，还开发了两个相应版本的BLS，即MAEBLS和L-MAEBLS。大量实验验证了所提出的L-BLS的鲁棒性和有效性，并且MAE可以为相应版本的BLS提供更具代表性的特征节点。
