Affiliations: [a] Department of Scientific Research Management, Chengdu Polytechnic, Chengdu 610041, Sichuan, China | [b] Sichuan Radio and TV University, Chengdu 610017, Sichuan, China | [c] School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China | [d] College of Computer Science, Sichuan Normal University, Chengdu 610101, Sichuan, China
Correspondence:
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Corresponding author: Xingjiang Yang, Department of Scientific Research Management, Chengdu Polytechnic, Chengdu 610041, Sichuan, China. E-mail: [email protected].
Abstract: Extreme learning machine (ELM) has been proved to be an efficient and effective machine learning method for pattern classification and regression. However, ELM is mainly applied to traditional supervised learning problems. ELM is not commonly used in multi-label image classification. In this paper, we propose a joint graph regularized extreme learning machine (JGELM) by simultaneously considering the feature information and label correlation of data. Specifically, we exploit the feature distance and label correlation in the local neighborhood. To this end, a joint graph regularizer based on a newly designed graph Laplacian to characterize both properties is formulated and incorporated into the ELM objective. Four popular multi-label image data sets are employed to test the proposed method. The experimental results show that JGELM are competitive with state-of-the-art multi-label classification algorithms in terms of accuracy and efficiency.
