一种改进的邻域保持嵌入算法

Improved Neighborhood Preserving Embedding Algorithm

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。

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 recognition 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 that the recognition rate of RNPE algorithm is significantly higher than NPE algorithm and other traditional algorithms.