Regularized discriminative broad learning system for image classification
作者：
Highlights：
• Three regularized discriminative BLS models are proposed according to the ɛɛ-dragging technique and regularization theory. These models can simultaneously learn more flexible labels and compact features for better image classification.
• Efficient iterative algorithms are designed to optimize the three proposed models. Moreover, a solid theoretical analysis and experimental verification are carried out to fully demonstrate its effectiveness.
• Diverse experiments are conducted to validate that our three proposed models have apparent superiority for image recognition in comparison with other state-of-the-art models.
摘要
•Three regularized discriminative BLS models are proposed according to the ɛɛ-dragging technique and regularization theory. These models can simultaneously learn more flexible labels and compact features for better image classification.•Efficient iterative algorithms are designed to optimize the three proposed models. Moreover, a solid theoretical analysis and experimental verification are carried out to fully demonstrate its effectiveness.•Diverse experiments are conducted to validate that our three proposed models have apparent superiority for image recognition in comparison with other state-of-the-art models.
论文关键词：Broad learning system,Discriminative,Relaxed regression targets,Row sparsity,Optimization
论文评审过程：Received 8 April 2022, Revised 17 June 2022, Accepted 18 June 2022, Available online 24 June 2022, Version of Record 2 July 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2022.109306
