正则化流形信息极端学习机

刘德山; 楚永贺; 闫德勤

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

您当前的位置：

首页 >

文章列表页 >

正则化流形信息极端学习机

学术论文 | 更新时间：2024-06-05

- 正则化流形信息极端学习机
- Regularized manifold information extreme learning machine
- 通信学报 2016年37卷第11期页码：57-67
- 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：
  
  [ "刘德山（1970-），男，辽宁辽阳人，辽宁师范大学副教授，主要研究方向为模式识别、数据挖掘。" ]
  [ "楚永贺（1989-），男，河南郑州人，辽宁师范大学硕士生，主要研究方向为数据降维、机器学习等。" ]
  [ "闫德勤（1962-），男，辽宁沈阳人，辽宁师范大学教授，主要研究方向为模式识别、机器学习等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61105085);辽宁省教育厅基金资助项目(L2014427)
- DOI：10.11959/j.issn.1000-436x.2016213
  中图分类号： TP18
- 网络首发：2016-11，
  
  纸质出版：2016-11-25
- 稿件说明：
移动端阅览
刘德山, 楚永贺, 闫德勤. 正则化流形信息极端学习机[J]. 通信学报, 2016,37(11):57-67.

De-shan LIU, Yong-he CHU, De-qin YAN. Regularized manifold information extreme learning machine[J]. Journal on Communications, 2016, 37(11): 57-67.
刘德山, 楚永贺, 闫德勤. 正则化流形信息极端学习机[J]. 通信学报, 2016,37(11):57-67. DOI： 10.11959/j.issn.1000-436x.2016213.

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.

关键词

Keywords

references

TANG J , DENG C , HUANG G B . Extreme learning machine for multilayer perception [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2015 ,( 99 ): 1 - 13 .

HUANG G B , ZHOU H M , DING X J , et al . Extreme learning machine for regression and multiclass classification [J ] . IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics , 2012 , 42 ( 2 ): 513 - 529 .

HUANG G B , CHEN L , SIEW C K . Universal approximation using incremental constructive feedforward networks with random hidden nodes [J ] . IEEE Trans Neural Networks , 2006 , 17 ( 4 ): 879 - 892 .

ZHANG R , LAN Y , HUANG G B , et al . Universal approximation of extreme learning machine with adaptive growth of hidden nodes [J ] . IEEE Trans Neural Networks Learn Systems , 2012 , 23 ( 2 ): 365 - 371 .

FERNÁNDEZ-DELGADO M , CERNADAS E , BARRO S , et al . Direct kernel perceptron (DKP): ultra-fast kernel ELM-based classifi-cation with noniterative closed-form weight calculation [J ] . Neural Networks , 2014 , 50 : 60 - 71 .

HUANG G , SONG S J . Semi-supervised and unsupervised extreme learning machines [J ] . IEEE Transactions on Cybernetics , 2014 , 44 ( 12 ): 2405 - 2417 .

HUANG G B , ZHU Q Y , SIEW C K . Extreme learning machine:theory and applications [J ] . Neurocomputing . 2006 , 70 : 489 - 501 .

VAPNIK V N . Statistical learning theory [J ] . Encyclopedia of the Sciences of Learning , 2010 , 41 ( 4 ): 3185 - 3185 .

HUANG G B , ZHOU H M , DING X J , et al . Extreme learning ma-chine for regression and multiclass classification [J ] . IEEE Transac-tions on Systems, Man, and Cybernetics, Part B: Cybernetics , 2012 , 42 ( 2 ): 513 - 529 .

WANG X Z , SHAO Q Y , MIAO Q , et al . Architecture selection for networks trained with extreme learning machine using localized gen-eralization error model [J ] . Neurocomputing , 2013 , 102 : 3 - 9 .

ZHAO J W , WANG Z H , PARK D S . Online sequential extreme learning machine with forgetting mechanism [J ] . Neurocomputing , 2012 , 87 : 79 - 89 .

RONG H J , HUANG G B , SUNDARARAJAN N , et al . Online se-quential fuzzy extreme learning machine for function approximation and classification problems [J ] . IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics , 2009 , 39 ( 4 ): 1067 - 1072 .

LIANG N Y , HUANG G B , SARATCHANDRAN P , et al . A fast and accurate online sequential learning algorithm for feedforward networks [J ] . IEEE Transactions on Neural Networks , 2006 , 17 ( 6 ): 1411 - 1423 .

ZONG W W , HUANG G B , CHEN Y . Weighted extreme learning machine for imbalance learning [J ] . Neurocomputing , 2013 , 101 : 229 - 242 .

YU Q , MICHE Y , EIROLA E , et al . Regularized extreme learning machine for regression with missing data [J ] . Neurocomputing , 2013 , 102 : 45 - 51 .

MAN Z H , WANG D H , CAO Z W , et al . Robust single-hidden layer feedforward network-based pattern classifier [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2012 , 23 ( 12 ): 1974 - 1986 .

PENG Y , LU B L . Discriminative graph regularized extreme learning machine and its application to face recognition [J ] . Neurocomputing , 2015 , 149 : 340 - 353 .

BELKIN M , NIYOGI P . Laplacian eigenmaps for dimensionality reduction and data representation [J ] . Neural Computation , 2003 , 15 ( 6 ): 1373 - 1396 .

HE X , NIYOGI P . Locality preserving projections [J ] . Advances in Neural Information Processing Systems , 2004 , 17 : 153 - 160 .

LI H , JIANG T , ZHANG K . Efficient robust feature extraction by maximum margin criterion [J ] . Advances in Neural Information Proc-essing Systems , 2003 , 144 : 71 - 78

LIU S , FENG L , XIAO Y . Robust activation function and its applica-tion: semi-supervised kernel extreme learning method [J ] . Neurocom-puting , 2014 , 144 : 318 - 328 .

HUANG G B . An insight into extreme learning machines: random neurons, random features and kernels [J ] . Cognitive Computation , 2014 , 6 : 376 - 390 .

HINTON G E , OSINDERO S . A fast learning algorithm for deep belief nets [J ] . Neural Computation , 2006 , 18 ( 7 ): 1527 - 1554 .

浏览量

1545

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于数据特征的成员推理增强攻击方法

IP地理定位技术综述：理论、方法与应用创新

基于机器学习的加密流量分类研究综述

面向代码重用检测的程序语义分析模型

工业互联网流量分析技术综述