Regularized manifold information extreme learning machine

De-shan LIU; Yong-he CHU; De-qin YAN

doi:10.11959/j.issn.1000-436x.2016213

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Regularized manifold information extreme learning machine

academic paper | 更新时间：2024-06-05

- Regularized manifold information extreme learning machine
- Journal on Communications Vol. 37, Issue 11, Pages: 57-67(2016)
- 作者机构：
  
  辽宁师范大学计算机与信息技术学院，辽宁大连 116081
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61105085);Liaoning Provincial Department of Educa-tion Project(L2014427)
- DOI：10.11959/j.issn.1000-436x.2016213
  CLC： TP18
- Online First：2016-11，
  
  Published：25 November 2016
- 稿件说明：
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De-shan LIU, Yong-he CHU, De-qin YAN. Regularized manifold information extreme learning machine[J]. Journal on Communications, 2016, 37(11): 57-67.
DOI：

De-shan LIU, Yong-he CHU, De-qin YAN. Regularized manifold information extreme learning machine[J]. Journal on Communications, 2016, 37(11): 57-67. DOI： 10.11959/j.issn.1000-436x.2016213.

摘要

基于流形学习的思想和理论方法，提出刻画流形信息的正则化的极端学习机(MELM)算法。该算法利用流形信息刻画数据的几何结构和判别信息，克服 ELM 在有限样本上学习不充分的问题；能够有效提取数据样本的判别信息避免数据样本信息重叠；利用最大边际准则有效解决类间散度矩阵和类内散度矩阵的奇异问题。为验证所提方法的有效性，实验使用普遍应用的图像数据，将 MELM 与 ELM 以及相关最新算法 RAFELM、GELM进行识别率和计算效率的对比。实验结果表明，该算法能够显著提高 ELM 的分类准确率和泛化能力，并且优于其他相关算法。

Abstract

By exploiting the thought of manifold learning and its theoretical method

a regularized manifold information ex-treme learning machine algorithm aimed to depict and fully utilize manifold information was proposed. The proposed algo-rithm exploited the geometry and discrimination manifold information of data to perform network of ELM. The proposed algorithm could overcome the problem of the overlap of information. Singular problems of inter-class and within-class were solved effectively by using maximum margin criterion. The problem of inadequate learning with limited samples was solved. In order to demonstrate the effectiveness

comparative experiments with ELM and the related update algorithms RAFELM

GELM were conducted using the commonly used image data. Experimental results show that the proposed algorithm can significantly improve the generalization performance of ELM and outperforms the related update algorithms.

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references

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