Ensemble extreme learning machine and sparse representation classification
Ensemble extreme learning machine and sparse representation classification
复制标题
集成极限学习机和稀疏表示分类
DOI：
10.1016/j.jfranklin.2016.08.024
复制
发表时间：
2016-11-01
影响因子：
4.1
通讯作者：
Luo, Minxia
中科院分区：
文献类型：
--
作者：
Cao, Jiuwen;Hao, Jiaoping;Luo, Minxia
极限学习机（ELM）结合稀疏表示分类（ELM- src）是近年来发展起来的一种图像分类方法。然而，在ELM- src中，使用带有随机隐藏参数的单一ELM网络可能会导致泛化和数据分区性能不稳定。为了解决这一问题，本文提出了一种增强的基于集成的ELM和SRC算法（En-SRC）。En-SRC不是使用单个ELM的输出来决定数据分区的阈值，而是采用多个集成来增强分类器的可靠性。与ELM-SRC不同，本文对En-SRC的数据分区阈值选择进行了理论分析。本文还对基于集成的正则化ELM进行了扩展。在多个基准分类数据库上的实验表明，与ELM-SRC方法相比，该方法具有更好的分类性能和更低的计算复杂度。(C) 2016富兰克林研究所。Elsevier Ltd.出版。版权所有。
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. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.