Retargeted broad learning systems for image classification

作     者：Jin, Junwei Zhu, Xianzheng Geng, Yun Liu, Jiahang Li, Yanting Liang, Jing Chen, C. L. Philip Li, Peng 

作者机构：Henan Univ Technol Key Lab Grain Informat Proc & Control Minist Educ Zhengzhou Peoples R China Henan Univ Technol Henan Key Lab Grain Storage Informat Intelligent P Zhengzhou Peoples R China Henan Univ Technol Sch Artificial Intelligence & Big Data Zhengzhou 450001 Peoples R China Henan Univ Technol Inst Complex Sci Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Light Ind Coll Comp Sci & Technol Zhengzhou 450001 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China 

出 版 物：《DIGITAL SIGNAL PROCESSING》 (Digital Signal Process Rev J)

年 卷 期：2025年第159卷

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：National Natural Science Foundation of China [62106068, 62073123] Science and Technology Research Project of Province Cultivation Project Tuoxin Team in Henan University of Technology Technology Innovation Talent Project of Henan Province University [25HASTIT028] Key research and development project in Henan Province 62106233 62375078 KFJJ2022001 CSKFJJ-2024-4 

主　　题：Broad learning system Retargeted label space Sparse regularization Optimization Image classification 

摘      要：The Broad Learning System (BLS) is recognized for its adept balance between efficiency and accuracy, displaying notable performance in image classification tasks owing to its streamlined network architecture and effective learning methodology. However, it faces significant challenges due to two prominent deficiencies that notably impede its learning efficacy. Firstly, the rigid binary labeling strategy inherent in BLS-based models imposes constraints on the model s adaptability. Additionally, the resultant broad features often exhibit redundancy, posing a risk of incorporating extraneous features. To address these issues, this article proposes three refined BLS-based models. Initially, a retargeting methodology is integrated into the standard BLS framework to alleviate constraints on regression targets, introducing the 82-based retargeted BLS (L2ReBLS) model. Subsequently, to mitigate the adverse effects of redundant features, the 82,1 regularizer is adopted as a replacement for the Frobenius norm in feature selection, resulting in the L21ReBLS model. Furthermore, the projection matrix of BLS is concurrently constrained with 82 and 82,1 regularization method simultaneously. Efficient iterative optimization methodologies via the alternating direction method of multipliers are devised for the purpose of solving the proposed approaches. Ultimately, comprehensive experiments conducted on diverse image databases are to highlight the superior performance of our proposed approaches in comparison to other state-of-the-art classification algorithms.