博士生楚永贺论文被Complexity录用
新闻来源:IR实验室       发布时间:2019/4/2 21:00:25

  近日接到编辑部通知,博士生楚永贺的论文 " Globality-Locality Preserving Maximum Variance Extreme Learning Machine" Complexity录用,该期刊影响因子为1.829SCI期刊,中科院分区:2区。

摘要:

针对现有极限学习机方法不能较好地保持数据空间的几何结构信息或判别信息等问题,提出一种基于流形学习的全局-局部保持最大方差极限学习机(globality-locality preserving maximum variance extreme learning machine ,GLELM.GLELM在继承传统的ELM方法的特点的基础上,将线性判别分析(LDA)和局部保持投影(LPP)的基本原理引入到ELM中,充分考虑样本蕴含的判别信息以及样本间具有的全局和局部流形结构,从而在一定程度上优化分类器的投影方向。为了验证所提算法的有效性,使用UCI数据集和图像数据集进行实验。实验结果表明,GLELM的分类结果表现出良好的性能。.

Abstract:

An extreme learning machine (ELM) is a useful technique for machine learning; however, the existing extreme learning machine methods cannot exploit the geometric structure information or discriminate information of the data space well. Therefore, we propose a globality-locality preserving maximum variance extreme learning machine (GLELM) based on manifold learning. Based on the characteristics of the traditional ELM method, GLELM introduces the basic principles of linear discriminant analysis (LDA) and local preservation projection (LPP) into ELM, fully taking account of the discriminant information contained in the sample. This method can preserve the global and local manifold structures of data to optimize the projection direction of the classifier. Experiments on several widely used image databases and UCI datasets validate the performance of GLELM. The experimental results show that the proposed model achieves promising results compared to several state-of-the-art ELM algorithms.