Towards Compact Broad Learning System by Combined Sparse Regularization

Jianyu Miao, Tiejun Yang, Jun-Wei Jin, Lijun Sun, Lingfeng Niu and Yong Shi
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Jianyu Miao: Key Laboratory of Grain Information Processing and Control (HAUT), Ministry of Education, Zhengzhou 450001, P. R. China2Henan Key Laboratory of Grain Photoelectric Detection and Control (HAUT), Zhengzhou 450001, P. R. China3College of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, P. R. China
Tiejun Yang: Key Laboratory of Grain Information Processing and Control (HAUT), Ministry of Education, Zhengzhou 450001, P. R. China2Henan Key Laboratory of Grain Photoelectric Detection and Control (HAUT), Zhengzhou 450001, P. R. China3College of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, P. R. China
Jun-Wei Jin: Key Laboratory of Grain Information Processing and Control (HAUT), Ministry of Education, Zhengzhou 450001, P. R. China2Henan Key Laboratory of Grain Photoelectric Detection and Control (HAUT), Zhengzhou 450001, P. R. China3College of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, P. R. China
Lijun Sun: Key Laboratory of Grain Information Processing and Control (HAUT), Ministry of Education, Zhengzhou 450001, P. R. China2Henan Key Laboratory of Grain Photoelectric Detection and Control (HAUT), Zhengzhou 450001, P. R. China4College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, P. R. China
Lingfeng Niu: Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, P. R. China6School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, P. R. China
Yong Shi: Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, P. R. China7School of Electrical and Information Engineering, Southwest Minzu University, Chengdu 610041, P. R. China8College of Information Science and Technology, University of Nebraska at Omaha, NE 68182,USA

International Journal of Information Technology & Decision Making (IJITDM), 2022, vol. 21, issue 01, 169-194

Abstract: Broad Learning System (BLS) has been proven to be one of the most important techniques for classification and regression in machine learning and data mining. BLS directly collects all the features from feature and enhancement nodes as input of the output layer, which neglects vast amounts of redundant information. It usually leads to be inefficient and overfitting. To resolve this issue, we propose sparse regularization-based compact broad learning system (CBLS) framework, which can simultaneously remove redundant nodes and weights. To be more specific, we use group sparse regularization based on â„“2,1 norm to promote the competition between different nodes and then remove redundant nodes, and a class of nonconvex sparsity regularization to promote the competition between different weights and then remove redundant weights. To optimize the resulting problem of the proposed CBLS, we exploit an efficient alternative optimization algorithm based on proximal gradient method together with computational complexity. Finally, extensive experiments on the classification task are conducted on public benchmark datasets to verify the effectiveness and superiority of the proposed CBLS.

Keywords: Broad learning system (BLS); group sparsity; nonconvex regularization; proximal gradient method (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0219622021500553

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