Joint graph regularized extreme learning machine for multi-label image classification

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.