A new weighted extreme learning machine based on elastic net regularization embedded exponential regularized discriminative dictionary learning for image classification

It is well known that discriminative sparse representation can significantly improve the performance of image classification. However, there remain several tricky issues to be addressed due to the unsatisfied performance and high time consumption. In this paper, a novel classification framework called weighted extreme learning machine exponential regularized discriminative dictionary learning (WELM-ERDDL) is proposed to address these issues. The main contributions of this paper include (1) the WELM is embedded with ERDDL via exponential regularized linear discriminative analysis (ERLDA) for feature mappings while enabling nonlinear and diverse feature representation; (2) in the ELM learning process, the elastic net regularization is utilized to optimize more robust and meaningful output weights; (3) an effective weight update rule is designed for WELM. To verify the effectiveness of the proposed method, several experiments are conducted on real-world image classification databases. The results show that the proposed WELM-ERDDL framework is even more efficient than other state-of-the-art algorithms in general.