Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 255-258.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Improved Neighborhood Preserving Embedding Algorithm

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

  1. College of Mathematics,Liaoning Normal University,Dalian,Liaoning 116029,China
  • Online:2018-06-20 Published:2018-08-03

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

CLC Number: 

  • TP391
[1]李瑞敏,陆化普.基于WebGIS的智能交通管理指挥调度系统.计算机工程,2007,33(21):232-234.
[2]SCHMITT E J,JULA H.Vehicle Route Guidance Systems: Classification and Comparison∥Proc.of IEEE Intelligent Transportation Systems Conference.2006:242-247.
[3]李旭,舒薇,李铭璐,等.车载传感器网络中的流量数据处理∥第17届计算机通信与网络国际会议.IEEE Press,2008:1-5.
[4]ROWEIS S T,SAUL L K.Nonlinear dimensionality reduction by locally linear embedding[J].Science,2000,290(5500):2323-2326.
[5]SEUNG H S,LEE D D.Cognition-the manifold ways of perception[J].Science,2000,290(5500):2268.
[6]BELKIN M,NIYOGI P.Laplacianeigenmaps for dimensionality reduction and data representation[J].Neural Computation,2003,15(6):1373-1396.
[7]TENENBAUM J B,DE SILVA V,LANGFORD J C.A global geometric framework fornonlinear dimensionality reduction[J].Science,2000,290(5500):2319-2324.
[8]SONG B,TAN S,SHI H.Process monitoring via enhanced neighborhood preserving embedding[J].Control Engineering Practice,2016,50:48-56.
[9]LIANG J Z,CHEN C,YI Y F,et al.Bilateral Two-Dimensional Neighborhood[C]∥Preserving Discriminant Embedding for Face Recognition.2017:17201-17212.
[10]冯林,刘胜蓝,张晶,等.高维数据中鲁棒激活函数的极端学习机及线性降维.计算机研究与发展,2014,51(6):1331-1340.
[11]BENOIT F,VAN HEESWIJK M,MICHE Y,et al.Feature selection for nonlinear models with extreme learning machines[J].Neurocomputing,2013,102(2):111-124.
[12]PENG Y,LU B L.Discriminative graph regularized extreme learning machine and its application to face recognition[J].Neurocomputing,2015,149(PA):340-353.
[13]HUANG G B.An Insight into Extreme Learning Machines: Random Neurons,Random Features and Kernels.Cognitive Computation,2014,6(3):376-390.
[14]戴礼荣,张仕良,黄智颖.基于深度学习的语音识别技术现状与展望.数据采集与处理,2017,32(2):221-231.
[15]TANG J,DENG C,HUANG G B.Extreme learning machine for multilayer perceptron[J].IEEE Transactions on Neural Networks and Learning Systems,2017,27(4):809-821.
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