Ensemble extreme learning machine and sparse representation classification. Issue 17 (November 2016)

• Record Type: Journal Article
• Authors: Cao, Jiuwen; Hao, Jiaoping; Lai, Xiaoping; Vong, Chi-Man; Luo, Minxia
• Is Part Of: Journal of the Franklin Institute. Volume 353:Issue 17(2016:Nov.)
• Page Start: 4526
• Page End: 4541
• Publication Date: 2016-11
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2016.08.024
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: Extreme learning machine (ELM) combining with sparse representation classification (ELM-SRC) has been developed for image classification recently. However, employing a single ELM network with random hidden parameters may lead to unstable generalization and data partition performance in ELM-SRC. To alleviate this deficiency, we propose an enhanced ensemble based ELM and SRC algorithm (En-SRC) in this paper. Rather than using the output of a single ELM to decide the threshold for data partition, En-SRC incorporates multiple ensembles to enhance the reliability of the classifier. Different from ELM-SRC, a theoretical analysis on the data partition threshold selection of En-SRC is given. Extension to the ensemble based regularized ELM with SRC (EnR-SRC) is also presented in the paper. Experiments on a number of benchmark classification databases show that the proposed methods win a better classification performance with a lower computational complexity than the ELM-SRC approach.