一种改进的邻域保持嵌入算法

计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 255-258.

一种改进的邻域保持嵌入算法

娄雪,闫德勤,王博林,王族

辽宁师范大学数学学院辽宁大连116029

出版日期:2018-06-20 发布日期:2018-08-03
作者简介:娄雪(1993－),女,硕士生,主要研究方向为数据降维、机器学习等;闫德勤(1962－),男,博士,教授,主要研究方向为模式识别、机器学习等;王博林(1993－),女,硕士生,主要研究方向为数据降维、机器学习等;王族(1992－),女,硕士生,主要研究方向为数据降维、机器学习等。
基金资助:
国家自然基金(61105085),辽宁省教育厅项目(L2014427)资助

Improved Neighborhood Preserving Embedding Algorithm

LOU Xue,YAN De-qin,WANG Bo-lin,WANG Zu

College of Mathematics,Liaoning Normal University,Dalian,Liaoning 116029,China

Online:2018-06-20 Published:2018-08-03

摘要/Abstract

摘要： 邻域保持嵌入(NPE)是一种新颖的子空间学习算法,在降维的同时保持了样本集原有的局部邻域流形结构。为了进一步增强NPE在人脸识别和语音识别中的识别功能,提出了一种改进的邻域保持嵌入算法(RNPE)。在NPE的基础上通过引入类间权值矩阵,使得类间离散度最大,类内离散度最小,增加了样本类间散布约束。最后利用极端学习机(ELM)分类器进行分类,在Yale人脸库、Umist人脸库、Isolet语音库上的实验结果表明,RNPE算法的识别率明显高于NPE算法、LMMDE算法以及RAF-GE算法。

关键词: 邻域保持, 邻域嵌入, 人脸识别

Abstract: Neighborhood persistence embedding (NPE) is a novel subspace learning algorithm that preserves the original local neighborhood structure of the sample set while maintaining dimensionality.In order to further improve the re-cognition function of NPE in face recognition and speech recognition,this paper proposed an improved neighborhood preserving embedding algorithm (RNPE).On the basis of NPE,by introducing the interclass weight matrix,the dispersion between classes is the largest,the intra-class dispersion is the smallest,distribution constraint between the classes is increased.The classification experiments are done by the extreme learning machine (ELM) classifier with Yale face database,Umist face database,Isolet speech database.The results show thatthe recognition rate of RNPE algorithm is significantly higher than NPE algorithm and other traditional algorithms.

Key words: Face recognition, Neighborhood embedding, Neighborhood preserving

中图分类号:

TP391

娄雪, 闫德勤, 王博林, 王族. 一种改进的邻域保持嵌入算法[J]. 计算机科学, 2018, 45(6A): 255-258. https://doi.org/

LOU Xue, YAN De-qin, WANG Bo-lin, WANG Zu. Improved Neighborhood Preserving Embedding Algorithm[J]. Computer Science, 2018, 45(6A): 255-258. https://doi.org/

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